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Syed Sajid Ullah

Doctoral Research Fellow

Department of Information and Communication Technology

Email: syed.s.ullah@uia.no
Phone: +47 37233753

Office A2090 (Jon Lilletuns vei 9, 4879 Grimstad, Norway)

Areas of responsibility

Working as a researcher in NIST projects of Named Data Networking and Quantum Cryptography. I have many publications in fields of ICT and computer science. I am working as a reviewer more then 16 journals. I am Editorial Board member of some quality journals

Research

For research contributions, visit my profiles of Researchgate and Google Scholar

Selected publications

Check my Researchgate and Google Scholar

Short biography

1. Visiting Lecture in Hazara University Mansehra, Pakistan 2017 to 2020

2. Research Fellow in Villanova University, USA 2021

3. Researcher in NIST, USA

Ahmad, Ijaz; Wan, Zhong; Ahmad, Ashfaq & Sajid Ullah, Syed (2024). A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things. Mathematics. ISSN 2227-7390. 12(10). doi: 10.3390/math12101437.
Ali, Muhammad; Imran, Muhammad; Baig, Muhammad Shamim; Shah, Adil; Sajid Ullah, Syed & Alroobaea, Roobaea [Show all 7 contributors for this article] (2024). Intelligent Control Shed Poultry Farm System Incorporating With Machine Learning. IEEE Access. ISSN 2169-3536. 12, p. 58168–58180. doi: 10.1109/ACCESS.2024.3391822.
Haque, Ehtisham Ul; Shah, Adil; Iqbal, Jawaid; Sajid Ullah, Syed; Alroobaea, Roobaea & Hussain, Saddam (2024). A scalable blockchain based framework for efficient IoT data management using lightweight consensus. Scientific Reports. ISSN 2045-2322. 14(1). doi: 10.1038/s41598-024-58578-7.
Hashmi, Asad Ul Haq; Mir, Ghias Uddin; Sattar, Kashif; Sajid Ullah, Syed; Alroobaea, Roobaea & Iqbal, Jawaid [Show all 7 contributors for this article] (2024). Effects of IoT Communication Protocols for Precision Agriculture in Outdoor Environments. IEEE Access. ISSN 2169-3536. 12, p. 46410–46421. doi: 10.1109/ACCESS.2024.3381522.
Sajid Ullah, Syed; Oleshchuk, Vladimir & Gardiyawasam Pussewalage, Harsha (2023). A Lightweight Access Control Scheme with Attribute Policy for Blockchain-Enabled Internet of Things. In De Capitani di Vimercati, Sabrina & Samarati, Pierangela (Ed.), Proceedings of the 20th International Conference on Security and Cryptography. SciTePress. ISSN 978-989-758-666-8. p. 528–539. doi: 10.5220/0012138900003555.
Sajid Ullah, Syed; Oleshchuk, Vladimir & Gardiyawasam Pussewalage, Harsha (2023). A survey on blockchain envisioned attribute based access control for internet of things: Overview, comparative analysis, and open research challenges. Computer Networks. ISSN 1389-1286. 235, p. 1–16. doi: 10.1016/j.comnet.2023.109994. Full text in Research Archive
Sajid Ullah, Syed; Hussain, Saddam; Ali, Ihsan & Mastorakis, Spyridon (2023). A Secure Ndn-based Architecture For Electronic Voting In 6g. IEEE Communications Standards Magazine. ISSN 2471-2825. 7(4), p. 20–26. doi: 10.1109/MCOMSTD.0003.2200074.
Ayub, Nasir; Tayyaba, NN; Hussain, Saddam; Sajid Ullah, Syed & Iqbal, Jawaid (2023). An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification. Algorithms. ISSN 1999-4893. 16(12). doi: 10.3390/a16120548. Full text in Research Archive
Salman Bukhari, Syed Muhammad; Raza Moosavi, Syed Kumayl; Zafar, Muhammad Hamza; Mansoor, Majad; Mohyuddin, Hassan & Sajid Ullah, Syed [Show all 8 contributors for this article] (2023). Federated transfer learning with orchard-optimized Conv-SGRU: A novel approach to secure and accurate photovoltaic power forecasting. Renewable Energy Focus. ISSN 1755-0084. 48. doi: 10.1016/j.ref.2023.100520. Full text in Research Archive
Sajid Ullah, Syed; Khan, Aizaz; Hussain, Saddam; Alsafyani, Majed; Alroobaea, Roobaea & Algarni, Sultan (2023). Sensitivity of atom based on slow light propagation and rotary photon drag: an efficient and secure model for quantum-based sensors in Internet of Things. Optical and Quantum Electronics. ISSN 0306-8919. 55(14). doi: 10.1007/s11082-023-05544-8.
Idrees, Muhammad; Khalid, Sohail; Rehman, Muhammad Abdul; Sajid Ullah, Syed; Hussain, Saddam & Iqbal, Jawaid (2023). Design of Multiplexers for IoT-Based Applications Using Stub-Loaded Coupled-Line Resonators. Micromachines. ISSN 2072-666X. 14(10). doi: 10.3390/mi14101821. Full text in Research Archive Show summary
This paper presents the design of microstrip-based multiplexers using stub-loaded coupled-line resonators. The proposed multiplexers consist of a diplexer and a triplexer, meticulously engineered to operate at specific frequency bands relevant to IoT systems: 2.55 GHz, 3.94 GHz, and 5.75 GHz. To enhance isolation and selectivity between the two passband regions, the diplexer incorporates five transmission poles (TPs) within its design. Similarly, the triplexer filter employs seven transmission poles to attain the desired performance across all three passbands. A comprehensive comparison was conducted against previously reported designs, considering crucial parameters such as size, insertion loss, return loss, and isolation between the two frequency bands. The fabrication of the diplexer and triplexer was carried out on a compact Rogers Duroid 5880 substrate. The experimental results demonstrate an exceptional performance, with the diplexer exhibiting a low insertion loss of 0.3 dB at 2.55 GHz and 0.4 dB at 3.94 GHz. The triplexer exhibits an insertion loss of 0.3 dB at 2.55 GHz, 0.37 dB at 3.94 GHz, and 0.2 dB at 5.75 GHz. The measured performance of the fabricated diplexer and triplexer aligns well with the simulated results, validating their effectiveness in meeting the desired specifications.
Mehmood, Arshad; Salah, Bashir; Sajid Ullah, Syed; Khan, Shahbaz & Khan, Razaullah (2023). Exploring synchronization and lift suppression in fluid flow around vibrating cylinder: a parallel CFD and global optimization investigation. Frontiers in Physics. ISSN 2296-424X. 11. doi: 10.3389/fphy.2023.1213274. Full text in Research Archive Show summary
In this study, we employ a parallel Computational Fluid Dynamics (CFD) code integrated with the VTDIRECT95 algorithm, a parallel deterministic global optimization method, to conduct global optimization for an oscillating circular cylinder. We conduct numerical simulations for the flow at a Reynolds number of 500 within the parameter range of 0.1≤Ax≤0.3 and 0.5fst≤fex≤2.5fst where Ax represents the inline oscillating amplitude, fex denotes the forcing oscillation frequency, and fst corresponds to the frequency of a stationary cylinder. To enhance computational efficiency, a combination of VTdirect and a CFD solver is utilized to efficiently identify the synchronization region, thereby reducing computational resources. The results reveal a significant reduction in the lift coefficient within the synchronized region compared to unsynchronized regimes. Furthermore, the study delves into the underlying flow physics behind synchronization and lift suppression. By synchronizing the shedding of vortices, their detrimental effects are nullified, resulting in a reduction in lift. Moreover, the research examines the influence of three-dimensional (3-D) flow by comparing 2-D and 3-D simulations at two different Reynolds numbers. It demonstrates that accounting for 3-D effects yields more accurate predictions of fluid behavior. Synchronization maps and root mean square (rms) lift coefficient plots illustrate the impact of Reynolds number and movement frequency on lift suppression. The findings indicate that achieving synchronization in 3-D flow necessitates stronger amplitudes and higher frequencies. At higher Reynolds numbers, the wake structures become unstable, leading to intricate vortical patterns. Consequently, the synchronization curve shifts towards higher amplitudes and frequencies in 3-D simulations. Understanding these phenomena is vital for reducing lift force in practical applications. This research significantly contributes to knowledge regarding synchronization and lift suppression in fluid flow around vibrating cylinders.
Shah, Syed Muhammad Ahmed Hassan; Ullah, Asad; Iqbal, Jawaid; Bourouis, Sami; Sajid Ullah, Syed & Hussain, Saddam [Show all 9 contributors for this article] (2023). Classifying and Localizing Abnormalities in Brain MRI Using Channel Attention Based Semi-Bayesian Ensemble Voting Mechanism and Convolutional Auto-Encoder. IEEE Access. ISSN 2169-3536. 11, p. 75528–75545. doi: 10.1109/ACCESS.2023.3294562. Full text in Research Archive Show summary
Adults who have brain tumors face a serious and potentially fatal challenge since the tumors rapidly growing malignant cells can seriously impair their physiological ability. In clinical practice, imaging techniques such as MRI, PET, and CT scans are widely used to determine the size, kind, and location of tumors. The goal of this research is to create a Computer-Aided Diagnostic (CAD) system that can automatically segment and classify brain tumors utilizing T1W-CE Magnetic Resonance Imaging (MRI) of the brain. The CAD system will involve two primary tasks: tumor classification, which determines the type of tumor depicted in the image, and tumor segmentation, which involves accurately determine the tumor region from the surrounding healthy tissue. By automating these processes, the proposed system aims to enhance the accuracy and effectiveness of brain tumor diagnosis and treatment planning. The classification of brain tumors into multiple classes is recognized as a complex challenge within the field of medical imaging. This research article proposes a model named VS-BEAM that can be used efficiently for clinical decision-making. The proposed VS-BEAM (Voting Based Semi-Supervised Bayesian Ensemble Attention Mechanism) model has been examined for a brain tumor’s multi-class classification. The VS-BEAM model achieved the highest level of accuracy possible. The proposed work achieves maximum sensitivity, specificity, and diagnostic accuracy compared to existing models using T1W-CE MRI images. A convolutional autoencoder is utilized for extracting tumors from MRI images. The accuracy obtained from testing data of 264 brain tumors was 98.91%, indicating that the method is effective and can be used in the clinical context to assist in detecting larger or even smaller tumors.
Miniappan, P.K.; Marimuthu, S.; Dharani Kumar, Kumar; Sharma, Shubham; Kumar, Abhinav & Salah, Bashir [Show all 7 contributors for this article] (2023). Exploring the mechanical, tribological, and morphological characteristics of areca fiber epoxy composites reinforced with various fillers for multifaceted applications. Frontiers in Materials. ISSN 2296-8016. doi: 10.3389/fmats.2023.1185215. Full text in Research Archive Show summary
This investigation is primarily concerned with the effect of fly ash, basalt powder, and tungsten carbide (WC) on the mechanical, microstructural, and tribological behaviour of areca fiber-reinforced composites. The fillers (5–10 wt. %) were included with the areca fiber epoxy reinforced composites. In comparison to areca fiber composites without fillers, the inclusion of fly ash, basalt powder, and WC increased the tensile strength by 33–48.2 MPa. The tensile strength of an A2 composite containing areca fiber (20 wt. %), epoxy (70 wt. %) and basalt powder (10 wt. %) was measured to be 48.2 MPa. Similarly, filler incorporation enhanced flexural, impact, and Shore D hardness properties by up to 21.25%, 13.18%, and 15.66%, respectively. Furthermore, the hybridization of fillers enhanced the mechanical properties and abrasion resistance of areca fiber reinforced composites. The inclusion of filler increases the load-carrying capability and adhesion, as determined by SEM. The river-like pattern demonstrates that ductile failure was dominated in the A5 [areca fiber (20 wt. %), epoxy (70 wt. %), fly ash (5 wt. %) and basalt powder (5 wt. %)] composites. A4 [areca fiber (20 wt. %), epoxy (70 wt. %), fly ash (5 wt. %) and tungsten carbide (5 wt. %)] composite has a lower wear resistance than all other composites. The hybrid filler-reinforced composite exhibits increased wear resistance as a result of the absence of wear detritus and textured surfaces.
Gul, Waheed; Akbar Shah, Syed Riaz; Khan, Afzal; Ahmad, Naveed; Ahmed, Sheraz & Ain, Noor [Show all 10 contributors for this article] (2023). Synthesis of graphene oxide (GO) and reduced graphene oxide (rGO) and their application as nano-fillers to improve the physical and mechanical properties of medium density fiberboard. Frontiers in Materials. ISSN 2296-8016. 10. doi: 10.3389/fmats.2023.1206918. Full text in Research Archive Show summary
Graphene is an advanced material in the carbon group and offers greater mechanical, electrical, structural, and optical properties. Graphene oxide (GO) and reduced graphene oxide (rGO) nanoparticles were synthesized and characterized and their special effects on enhancing the physio-mechanical characteristics of medium density fiberboard (MDF) were assessed. GO and rGO nanoparticles were added to urea formaldehyde (UF) resin at different weight percentages (1.0, 2.0, and 3.0 wt%) during the dosing process. To manufacture the MDF, nanofillers were created by sonication and combination with natural wood fibers. To observe the behavior of nanoparticles in the nanofillers, microstructure characterizations were conducted. The manufactured nano MDF samples underwent physical and mechanical testing. The incorporation of GO and rGO nanoparticles into UF resin led to significant improvements in the physical and mechanical properties of the MDF. The addition of GO and rGO nanoparticles at different weight percentages (1.0, 2.0, and 3.0 wt%) resulted in a range of improvements in thickness swelling (up to 53.3% and 35.2% for GO and rGO nanoparticles, respectively), water absorption (up to 23.3% and 63.15%, respectively), and thermal conductivity (up to 42.16% and 27.7%, respectively). Additionally, the internal bond and rupture modulus of the MDF was enhanced by 59.0% and 70.0%, respectively, for GO and 41.4% and 48.5% for rGO. The highest value of the modulus of rupture (MoR) was observed at a concentration of 3.0% of rGO nanoparticles (44.7 MPa). The findings also showed that thickness swelling (Ts) and water absorption (WA) exhibited directly proportional relationships for 3.0% GO and rGO. These results suggested that incorporating GO and rGO nanoparticles into UF resin can significantly improve the physical and mechanical properties of nano MDF.
Karim, Fazal; Haneef, Muhammad; Sajid Ullah, Syed; Alsafyani, Majed; Alroobaea, Roobaea & Algarni, Sultan [Show all 7 contributors for this article] (2023). Manipulation speed of light and giant phase shifting: a new quantum-based model for improving efficiency and security of internet of things. Optical and Quantum Electronics. ISSN 0306-8919. 55. doi: 10.1007/s11082-023-05074-3. Show summary
The Internet of Things (IoT) is transitioning towards Quantum Computing soon, relying on electromagnetic waves for data transfer within circuits and between IoT devices. This paper introduces a four-level atomic medium controlled by electromagnetic fields to manipulate the speed of light, resulting in subluminal and superluminal information transfer for IoT. Subluminal propagation transmits data at an average rate, while superluminal propagation transfers data faster than conventional methods. The differentiation between these two modes of propagation is determined by the sign of the group index, which is positive for subluminal and negative for superluminal. Our proposed model reports a maximum positive and negative group index of ± 5000. This modified group velocity to become vg=±c/5000 enhances IoT information transferring in quantum-based communication. The group delay time is measured to td=±10μs in the medium, which increases the capacity for IoT information storage. The maximum value of normal and anomalous phase shift is reported to Φ=±20000 radians at 60cm lengths of the medium; this effect is used to divide information in sub-set that protect IoT information from different types of attacks and losses.
Mehmood, Arshad; Akhtar, Kareem; Noor, Sahar; Zahir, M. Zeeshan; Ullah, Barkat & Khan, Razaullah [Show all 8 contributors for this article] (2023). Experimental study to optimize cold working, aging temperature, and time on the properties of AA6061 tubes: analysis using design of experiment. Frontiers in Materials. ISSN 2296-8016. 10. doi: 10.3389/fmats.2023.1199099. Full text in Research Archive Show summary
The purpose of this study is to examine the impact of various process parameters, such as cold work, aging temperature, and aging time, on the yield strength, ultimate tensile strength (UTS), and elongation of AA6061 tubes. The experimental plan is carried out, and the data is analyzed using Design Expert software. Main effects plots and interaction plots are generated to visually examine the effects of individual factors and the interaction between two factors on the output response variables. ANOVA analysis is conducted to assess the statistical significance of the model and individual model coefficients. The results reveal that all input factors had a significant impact on yield, whereas cold work and temperature and their interaction are significant for UTS. However, the model is not significant for elongation. The most notable finding is that the aging temperature’s effect is significant than the other two factors. These study findings can inform future experiments or process optimization efforts by considering the combined impact of these factors and their interactions. The study also found that the optimal temperature range is between 155°C to 170°C, along with a recommended cold work percentage of 10% or more and preferred time of above 10 h up to overage time. The model achieved an overall accuracy rate of over 90%, indicating its ability to predict the response variable with a high degree of precision
Gul, Waheed; Ahmad, Naveed; Mohammad, Shoaib; Salah, Bashir; Sajid Ullah, Syed & Khurram, Muhammad [Show all 7 contributors for this article] (2023). Impact of moisture content, closing speed, and pressurizing speed on the performance of medium density fiberboard (MDF). Frontiers in Materials. ISSN 2296-8016. 10. doi: 10.3389/fmats.2023.1195789. Full text in Research Archive
Khalid, Muhammad Irfan; Ehsan, Ibtisam; Al-Ani, Ayman Khallel; Iqbal, Jawaid; Hussain, Saddam & Sajid Ullah, Syed [Show all 7 contributors for this article] (2023). A Comprehensive Survey on Blockchain-Based Decentralized Storage Networks. IEEE Access. ISSN 2169-3536. 11, p. 10995–11015. doi: 10.1109/ACCESS.2023.3240237. Full text in Research Archive Show summary
Blockchains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted blocks without requiring a central authority or centralized controller. A prominent application of this technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future.
Hammad, Muhammad; Iqbal, Jawaid; Hassan, Ch Anwar ul; Hussain, Saddam; Sajid Ullah, Syed & Uddin, Mueen [Show all 9 contributors for this article] (2023). Blockchain-Based Decentralized Architecture for Software Version Control. Applied Sciences. ISSN 2076-3417. 13(5). doi: 10.3390/app13053066. Full text in Research Archive Show summary
Version control is an important component of configuration management, and most enterprise-level software uses different tools and technologies to manage the software version control such as CVS, Subversion, or Perforce. Following the success of bitcoin, the first practical application of blockchain, it is being implemented in other fields such as healthcare, supply chains, financial management, real estate, electoral systems, and so on. Blockchain’s core features include decentralization, immutability, and interminability. Most version control repositories are centralized and can be modified by external sources, implying that they are in danger of being corrupted or controlled. In this study, we present the BDA-SCV architecture for implementing a version control system in blockchain technology. Our proposed approach would replace the necessity for a centralized system, with a decentralized approach implemented in the blockchain using distributed file storage, for which we will use the InterPlanetary File System (IPFS), which is a distributed file system. The proof of authority (PoA) consensus algorithm will be used to approve the developer communicating modifications to the private blockchain network; the authority will only provide permission and will not be able to add, edit, or delete code files. For each change, a ledger block will be created with a reference to the file stored in the distributed repository. A block cannot be manipulated once it has been created. Smart contracts will be used to register developers, create blocks, and manage the repository. The suggested model is implemented using the Hyperledger Fabric network, and the developer and authorizer ends are built into the dotnet web application.
Hassan, Ch. Anwar Ul; Karim, Faten Khalid; Abbas, Assad; Iqbal, Jawaid; Elmannai, Hela & Hussain, Saddam [Show all 8 contributors for this article] (2023). A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies. Sustainability. ISSN 2071-1050. 15(5). doi: 10.3390/su15053982.
Hasnain, S M Mozammil; Chatterjee, Rajeshwari; Ranjan, Prabhat; Kumar, Gaurav; Sharma, Shubham & Kumar, Abhinav [Show all 8 contributors for this article] (2023). Performance, Emission, and Spectroscopic Analysis of Diesel Engine Fuelled with Ternary Biofuel Blends. Sustainability. ISSN 2071-1050. 15(9). doi: 10.3390/su15097415.
Zaheer, Shahzad; Anjum, Nadeem; Hussain, Saddam; Algarni, Abeer D.; Iqbal, Jawaid & Bourouis, Sami [Show all 7 contributors for this article] (2023). A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model. Mathematics. ISSN 2227-7390. 11(3). doi: 10.3390/math11030590. Full text in Research Archive Show summary
Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.
Hussain, Musharraf; Baloach, Naveed Khan; Ali, Gauhar; ElAffendi, Mohammed; Dhaou, Imed Ben & Sajid Ullah, Syed [Show all 7 contributors for this article] (2023). Hardware Trojan Mitigation Technique in Network-on-Chip (NoC). Micromachines. ISSN 2072-666X. 14(4). doi: 10.3390/mi14040828. Full text in Research Archive Show summary
Due to globalization in the semiconductor industry, malevolent modifications made in the hardware circuitry, known as hardware Trojans (HTs), have rendered the security of the chip very critical. Over the years, many methods have been proposed to detect and mitigate these HTs in general integrated circuits. However, insufficient effort has been made for hardware Trojans (HTs) in the network-on-chip. In this study, we implement a countermeasure to congeal the network-on-chip hardware design in order to prevent changes from being made to the network-on-chip design. We propose a collaborative method which uses flit integrity and dynamic flit permutation to eliminate the hardware Trojan inserted into the router of the NoC by a disloyal employee or a third-party vendor corporation. The proposed method increases the number of received packets by up to 10% more compared to existing techniques, which contain HTs in the destination address of the flit. Compared to the runtime HT mitigation method, the proposed scheme also decreases the average latency for the hardware Trojan inserted in the flit’s header, tail, and destination field up to 14.7%, 8%, and 3%, respectively.
Ali, Zulfiqar; Mahmood, Azhar; Khatoon, Shaheen; Alhakami, Wajdi; Sajid Ullah, Syed & Iqbal, Jawaid [Show all 7 contributors for this article] (2023). A Generic Internet of Things (IoT) Middleware for Smart City Applications. Sustainability. ISSN 2071-1050. 15(1). doi: 10.3390/su15010743.
Sridhar, V.; Ranga Rao, Rao; Hussain, Saddam; Sajid Ullah, Syed; Alroobaea, Roobaea & Abdelhaq, Maha [Show all 7 contributors for this article] (2023). Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security. Computers, Materials and Continua (CMC). ISSN 1546-2218. 74(1), p. 1693–1708. doi: 10.32604/cmc.2023.028129. Show summary
Nonorthogonal Multiple Access (NOMA) is incorporated into the wireless network systems to achieve better connectivity, spectral and energy effectiveness, higher data transfer rate, and also obtain the high quality of services (QoS). In order to improve throughput and minimum latency, a Multivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access (MRRWPBA-NOMA) technique is introduced for network communication. In the downlink transmission, each mobile device's resources and their characteristics like energy, bandwidth, and trust are measured. Followed by, the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e., Multivariate Renkonen Regression functions. Based on the classification, resource and trust-aware devices are selected for transmission. Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio, throughput, latency, packet loss rate, and energy efficiency, signaling overhead. The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.
Shah, Syed Mohsin Ali; Usman, Syed Muhammad; Khalid, Shehzad; Rehman, Ikram Ur; Anwar, Aamir & Hussain, Saddam [Show all 10 contributors for this article] (2022). An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications. Sensors. ISSN 1424-8220. 22(24). doi: 10.3390/s22249744. Full text in Research Archive
Khan, Abdur Rehman; Yasin, Amanullah; Usman, Syed Muhammad; Hussain, Saddam; Khalid, Shehzad & Sajid Ullah, Syed (2022). Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment. Electronics. ISSN 2079-9292. 11(24). doi: 10.3390/electronics11244147. Full text in Research Archive
Riaz, Sharjeel; Latif, Shahzad; Usman, Syed Muhammad; Sajid Ullah, Syed; Algarni, Abeer D. & Yasin, Amanullah [Show all 9 contributors for this article] (2022). Malware Detection in Internet of Things (IoT) Devices Using Deep Learning. Sensors. ISSN 1424-8220. 22(23). doi: 10.3390/s22239305. Full text in Research Archive Show summary
: Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.
Awan, Amna Waheed; Usman, Syed Muhammad; Khalid, Shehzad; Anwar, Aamir; Alroobaea, Roobaea & Hussain, Saddam [Show all 9 contributors for this article] (2022). An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals. Sensors. ISSN 1424-8220. 22(23). doi: 10.3390/s22239480.
Ashfaq, Tehreem; Khalid, Muhammad Irfan; Ali, Gauhar; Affendi, Mohammad El; Iqbal, Jawaid & Hussain, Saddam [Show all 10 contributors for this article] (2022). An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain. Sensors. ISSN 1424-8220. 22(19). doi: 10.3390/s22197263. Full text in Research Archive Show summary
In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.
Hassan, Ch. Anwar ul; Iqbal, Jawaid; Irfan, Rizwana; Hussain, Saddam; Algarni, Abeer D. & Bukhari, Syed Sabir Hussain [Show all 8 contributors for this article] (2022). Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers. Sensors. ISSN 1424-8220. 22(19). doi: 10.3390/s22197227. Full text in Research Archive Show summary
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
Ahmed, Naveed; Ngadi, Asri Bin; Sharif, Johan Mohamad; Hussain, Saddam; Uddin, Mueen & Rathore, Muhammad Siraj [Show all 11 contributors for this article] (2022). Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction. Sensors. ISSN 1424-8220. 22(20). doi: 10.3390/s22207896. Full text in Research Archive Show summary
A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.
Bukhari, Muhammad Mazhar; Sajid Ullah, Syed; Uddin, Mueen; Hussain, Saddam; Abdelhaq, Maha & Alsaqour, Raed (2022). An Intelligent Model for Predicting the Students' Performance with Backpropagation Neural Network Algorithm Using Regularization Approach. Human-centric Computing and Information Sciences. ISSN 2192-1962. 12. doi: 10.22967/HCIS.2022.12.044.
Iqbal, Jawaid; Bibi, Hajira; Amin, Noor Ul; Alsalman, Hussain; Sajid Ullah, Syed & Hussain, Saddam [Show all 7 contributors for this article] (2022). Efficient and Secure Key Management and Authentication Scheme for WBSNs Using CP-ABE and Consortium Blockchain. Journal of Sensors. ISSN 1687-725X. 2022. doi: 10.1155/2022/2419992. Full text in Research Archive
Hamid, Danish; Sajid Ullah, Syed; Iqbal, Jawaid; Hussain, Saddam; Hassan, Ch. Anwar Ul & Umar, Fazlullah (2022). A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream. Journal of Sensors. ISSN 1687-725X. 2022. doi: 10.1155/2022/8400622. Full text in Research Archive
Algarni, Musaad Darwish; Alroobaea, Roobaea; Almotiri, Jasem; Sajid Ullah, Syed; Hussain, Saddam & Umar, Fazlullah (2022). An Efficient Convolutional Neural Network with Transfer Learning for Malware Classification. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2022. doi: 10.1155/2022/4841741. Full text in Research Archive
Nayab, Nayab; Hussain, Saddam; Alabrah, Amerah; Sajid Ullah, Syed; Khattak, Hizbullah & Alfakih, Taha M. [Show all 7 contributors for this article] (2022). An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2022. doi: 10.1155/2022/4215441. Full text in Research Archive
Mian, Imdad Ahmad; Ul-Haq, Ijaz; Anwar, Aamir; Alroobaea, Roobaea; Sajid Ullah, Syed & Almansour, Fahad [Show all 7 contributors for this article] (2022). A Comprehensive Skills Analysis of Novice Software Developers Working in the Professional Software Development Industry. Complexity. ISSN 1076-2787. 2022. doi: 10.1155/2022/2631727. Full text in Research Archive Show summary
Measuring and evaluating a learner’s learning ability is always the focus of every person whose aim is to develop strategies and plans for their learners to improve the learning process. For example, classroom assessments, self-assessment using computer systems such as Intelligent Tutoring Systems (ITS), and other approaches are available. Assessment of metacognition is one of these techniques. Having the ability to evaluate and monitor one’s learning is known as metacognition. An individual can then propose adjustments to their learning process based on this assessment. By monitoring, improving, and planning their activities, learners who can manage their cognitive skills are better able to manage their knowledge about a particular subject. It is common knowledge that students’ metacognitive and self-assessment skills and abilities have been extensively studied, but no research has been carried out on the mistakes that novice developers make because they do not use their self-assessment abilities enough. ,is study aims to assess the metacognitive skills and abilities of novice software developers working in the industry and to describe the consequences of awareness of metacognition on their performance. In the proposed study, we experimented with novice software developers and collected data using Devskiller and a self-assessment log to analyze their use of self-regulation skills. ,e proposed study showed that when developers are asked to reflect upon their work, they become more informed about their habitual mistakes, and using a self-assessment log helps them highlight their repetitive mistakes and experiences which allows them to improve their performance on future tasks.
Ali, Wajid; Ali, Mehboob; Hussain, Israr; Sajid Ullah, Syed; Alroobaea, Roobaea & Hussain, Saddam [Show all 8 contributors for this article] (2022). A New Correlation Coefficient for T-Spherical Fuzzy Sets and Its Application in Multicriteria Decision-Making and Pattern Recognition. Journal of Sensors. ISSN 1687-725X. 2022. doi: 10.1155/2022/4471945. Full text in Research Archive Show summary
The goal of this paper is to design a new correlation coefficient for -spherical fuzzy sets (TSFSs), which can accurately measure the nature of correlation (i.e., positive and negative) as well as the degree of relationship between TSFS. In order to formulate our proposed idea, we had taken inspiration from the statistical concept of the correlation coefficient. While doing so, we firstly introduce the variance and covariance of two TSFS and then constructed our scheme using these two newly defined notions. The numerical value of our proposed correlation coefficient lies within the interval , as it should be from a statistical point of view, whereas the existing methods cannot measure the negative correlation between TSFS, as their numerical value falls within the interval , which is not reasonable both statistically and intuitively. This aspect has also been thoroughly demonstrated using some numerical examples. The comparison results witnessed the dominance and upper hand of our proposed method over the existing definitions, with reliable and better results. In order to demonstrate the feasibility, usefulness, and practical application, we applied our proposed scheme to solve technical and scientific problems of multicriteria decision-making and pattern recognition. The numerical results show that our proposed scheme is practically suitable, technically applicable, and intuitively reasonable.
Shuaib, Mohammed; Badotra, Sumit; Khalid, Muhammad Irfan; Algarni, Abeer D.; Sajid Ullah, Syed & Bourouis, Sami [Show all 9 contributors for this article] (2022). A Novel Optimization for GPU Mining Using Overclocking and Undervolting. Sustainability. ISSN 2071-1050. 14(14). doi: 10.3390/su14148708. Full text in Research Archive Show summary
: Cryptography and associated technologies have existed for a long time. This field is advancing at a remarkable speed. Since the inception of its initial application, blockchain has come a long way. Bitcoin is a cryptocurrency based on blockchain, also known as distributed ledger technology (DLT). The most well-known cryptocurrency for everyday use is Bitcoin, which debuted in 2008. Its success ushered in a digital revolution, and it currently provides security, decentralization, and a reliable data transport and storage mechanism to various industries and companies. Governments and developing enterprises seeking a competitive edge have expressed interest in Bitcoin and other cryptocurrencies due to the rapid growth of this recent technology. For computer experts and individuals looking for a method to supplement their income, cryptocurrency mining has become a big source of anxiety. Mining is a way of resolving mathematical problems based on the processing capacity and speed of the computers employed to solve them in return for the digital currency incentives. Herein, we have illustrated benefits of utilizing GPUs (graphical processing units) for cryptocurrency mining and compare two methods, namely overclocking and undervolting, which are the superior techniques when it comes to GPU optimization. The techniques we have used in this paper will not only help the miners to gain profits while mining cryptocurrency but also solve a major flaw; in order to mitigate the energy and resources that are consumed by the mining hardware, we have designed the mining hardware to simultaneously run longer and consume much less electricity. We have also compared our techniques with other popular techniques that are already in existence with respect to GPU mining.
Naveed, Muhammad; Arif, Fahim; Usman, Syed Muhammad; Anwar, Aamir; Hadjouni, Myriam & Elmannai, Hela [Show all 9 contributors for this article] (2022). A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2022. doi: 10.1155/2022/2215852. Full text in Research Archive Show summary
An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhanced intrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used for classification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, the suggested model receives training and is then evaluated. The proposed method was successful in achieving the following results: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%, and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstrate that the proposed model is superior to the performance of the other comparison approaches.
Ehsan, Ibtisam; Mumtaz, Asia; Khalid, Muhammad Irfan; Iqbal, Jawaid; Hussain, Saddam & Sajid Ullah, Syed [Show all 7 contributors for this article] (2022). Internet of Things-Based Fire Alarm Navigation System: A Fire-Rescue Department Perspective. Mobile Information Systems. ISSN 1574-017X. 2022. doi: 10.1155/2022/3830372. Full text in Research Archive Show summary
In the past few years, fire alarm systems have become increasingly sophisticated and more capable and reliable. The two main objectives are the protection of life and property. As a result of state and local codes, fire protection has become more concerned with life safety over the past two decades. Several safety measures have been implemented to address the problems caused by the fires and reduce the number of fatalities and property damage. Our project is to develop and review a fire alarm navigation system and application that uses the internet of things. Fire alarm systems are designed to warn people about fires in advance so that they can evacuate the fire-affected area and take immediate action to control the fire. There will be a GPS module, a flame sensor, a smoke sensor, buzzers, LEDs, and a GSM module to ensure early notification to authorities and fire stations. The aim is to reduce the loss of lives and property. A questionnaire was designed to conduct a brief survey in a multinational sports production company in Sialkot, Pakistan, regarding the IoT fire alarm navigation system. Besides installing the system in the factory, we compare the results with fire incident response time with and without this system at rescue 1122 fire head station.
Hassan, Ch Anwar Ul; Iqbal, Jawaid; Khan, Muhammad Sufyan; Hussain, Saddam; Akhunzada, Adnan & Ali, Mudabbir [Show all 9 contributors for this article] (2022). Design and Implementation of Real-Time Kitchen Monitoring and Automation System Based on Internet of Things. Energies. ISSN 1996-1073. 15(18). doi: 10.3390/en15186778. Full text in Research Archive Show summary
Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses.
Sajid Ullah, Syed; Hussain, Saddam; Uddin, Mueen; Alroobaea, Roobaea; Iqbal, Jawaid & Baqasah, Abdullah M. [Show all 8 contributors for this article] (2022). A Computationally Efficient Online/Offline Signature Scheme for Underwater Wireless Sensor Networks. Sensors. ISSN 1424-8220. 22(14). doi: 10.3390/s22145150. Full text in Research Archive Show summary
Underwater wireless sensor networks (UWSNs) have emerged as the most widely used wireless network infrastructure in many applications. Sensing nodes are frequently deployed in hostile aquatic environments in order to collect data on resources that are severely limited in terms of transmission time and bandwidth. Since underwater information is very sensitive and unique, the authentication of users is very important to access the data and information. UWSNs have unique communication and computation needs that are not met by the existing digital signature techniques. As a result, a lightweight signature scheme is required to meet the communication and computa‑ tion requirements. In this research, we present a Certificateless Online/Offline Signature (COOS) mechanism for UWSNs. The proposed scheme is based on the concept of a hyperelliptic curves cryptosystem, which offers the same degree of security as RSA, bilinear pairing, and elliptic curve cryptosystems (ECC) but with a smaller key size. In addition, the proposed scheme was proven secure in the random oracle model under the hyperelliptic curve discrete logarithm problem. A se‑ curity analysis was also carried out, as well as comparisons with appropriate current online/offline signature schemes. The comparison demonstrated that the proposed scheme is superior to the exist‑ ing schemes in terms of both security and efficiency. Additionally, we also employed the fuzzy‑based Evaluation‑based Distance from Average Solutions (EDAS) technique to demonstrate the effective‑ ness of the proposed scheme.
Aslam, Muhammad Haseeb; Usman, Syed Muhammad; Khalid, Shehzad; Anwar, Aamir; Alroobaea, Roobaea & Hussain, Saddam [Show all 9 contributors for this article] (2022). Classification of EEG Signals for Prediction of Epileptic Seizures. Applied Sciences. ISSN 2076-3417. 12(14). doi: 10.3390/app12147251. Full text in Research Archive Show summary
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8% , and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.
Bharany, Salil; Sharma, Sandeep; Frnda, Jaroslav; Shuaib, Mohammed; Khalid, Muhammad Irfan & Hussain, Saddam [Show all 8 contributors for this article] (2022). Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS. Drones. ISSN 2504-446X. 6(8). doi: 10.3390/drones6080193. Full text in Research Archive Show summary
Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.
Khan, Sardar Waqar; Hafeez, Qasim; Khalid, Muhammad Irfan; Alroobaea, Roobaea; Hussain, Saddam & Iqbal, Jawaid [Show all 8 contributors for this article] (2022). Anomaly Detection in Traffic Surveillance Videos Using Deep Learning. Sensors. ISSN 1424-8220. 22(17). doi: 10.3390/s22176563. Full text in Research Archive Show summary
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.
Hussain, Israr; Hussain, Dostdar; Kohli, Rashi; Ismail, Muhammad; Hussain, Saddam & Sajid Ullah, Syed [Show all 9 contributors for this article] (2022). Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics. Mobile Information Systems. ISSN 1574-017X. 2022, p. 1–10. doi: 10.1155/2022/2847580. Full text in Research Archive Show summary
Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.
Hussain, Dostdar; Ismail, Muhammad; Hussain, Israr; Alroobaea, Roobaea; Hussain, Saddam & Sajid Ullah, Syed (2022). Face Mask Detection Using Deep Convolutional Neural Network and MobileNetV2-Based Transfer Learning. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2022. doi: 10.1155/2022/1536318. Full text in Research Archive
Rathore, Muhammad Siraj; Ahmad, Naveed; Kohli, Rashi; Iqbal, Jawaid; Alroobaea, Roobaea & Hussain, Saddam [Show all 8 contributors for this article] (2022). In the Direction of Service Guarantees for Virtualized Network Functions. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2022. doi: 10.1155/2022/5507845. Full text in Research Archive Show summary
The trend of consolidating network functions from specialized hardware to software running on virtualization servers brings significant advantages for reducing costs and simplifying service deployment. However, virtualization techniques have significant limitations when it comes to networking as there is no support for guaranteeing that network functions meet their service requirements. In this paper, we present a design for providing service guarantees to virtualized network functions based on rate control. The design is a combination of rate regulation through token bucket filters and the regular scheduling mechanisms in operating systems. It has the attractive property that traffic profiles are maintained throughout a series of network functions, which makes it well suited for service function chaining. We discuss implementation alternatives for the design and demonstrate how it can be implemented on two virtualization platforms: LXC containers and the KVM hypervisor. To evaluate the design, we conduct experiments where we measure throughput and latency using IP forwarders (routers) as examples of virtual network functions. Two significant factors for performance are investigated: the design of token buckets and the packet clustering effect that comes from scheduling. Finally, we demonstrate how performance guarantees are achieved for rate-controlled virtual routers under different scenarios.
Ghawy, Mohammed Zaid; Amran, Gehad Abdullah; Alsalman, Hussain; Ghaleb, Eissa; Khan, Javed & Al-Bakhrani, Ali A. [Show all 9 contributors for this article] (2022). An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm. Wireless Communications & Mobile Computing. ISSN 1530-8669. 2022. doi: 10.1155/2022/8455065. Full text in Research Archive Show summary
Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.
Ehsan, Ibtisam; Irfan Khalid, Muhammad; Ricci, Laura; Iqbal, Jawaid; Alabrah, Amerah & Sajid Ullah, Syed [Show all 7 contributors for this article] (2022). A Conceptual Model for Blockchain-Based Agriculture Food Supply Chain System. Scientific Programming. ISSN 1058-9244. 2022. doi: 10.1155/2022/7358354. Full text in Research Archive Show summary
In agriculture supply chain management, traceability is a crucial aspect to ensure food safety for increasing customer loyalty and satisfaction. Lack of quality assurance in centralized data storage makes us move towards a new approach based on a decentralized system in which transparency and quality assurance is guaranteed throughout the supply chain from producer to consumer. The current supply chain model has some disadvantages like a communication gap between the entities of the supply chain and no information about the travel history and origin of the product. The use of technology improves the communication and relation between various farmers and stakeholders. Blockchain technology acquires transparency and traceability in the supply chain, provides transaction records traceability, and enhances security for the whole supply chain. In this paper, we present a blockchain-based, fully decentralized traceability model that ensures the integrity and transparency of the system. This new model eliminated most of the disadvantages of the traditional supply chain. For the coordination of all transactions in the supply chain, we proposed a decentralized supply chain model along with a smart contract.
Shah, Faiza; Liu, Yumin; Anwar, Aamir; Shah, Yasir; Alroobaea, Roobaea & Hussain, Saddam [Show all 7 contributors for this article] (2022). Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems. Security and Communication Networks. ISSN 1939-0114. 2022. doi: 10.1155/2022/7149902. Full text in Research Archive Show summary
Technology has turned into a significant differentiator in the money and traditional recordkeeping systems for the financial industry. To depict two customers as potential investors, it is mandatory to give the complex innovation that they anticipate and urge to purchase. In any case, it is difficult to keep on top of and be a specialist in each of the new advancements that are accessible. By reappropriating IT administrations, monetary administrations firms can acquire prompt admittance to the most recent ability and direction. Financial systems, along with machine learning (ML) algorithms, are vital for critical concerns like secure financial transactions and automated trading. These are the key to the provision of financial decisions for investors and stakeholders for the firms which are working with the trade credit (TC) approach, in Small and Medium Industries (SMEs). Huge and very sensitive data is processed in a limited time. The trade credit is a reason for more financial gains. The impact of TC with predictive machine learning algorithms is the reason why intelligent and safe revenue generation is the main target of the proposed study. That is, the combination of financial data and technology (FinTech) domains is a potential reason for sales growth and ultimately more profit.
Khalid, Muhammad Irfan; Iqbal, Jawaid; Alturki, Ahmad; Hussain, Saddam; Alabrah, Amerah & Sajid Ullah, Syed (2022). Blockchain-Based Land Registration System: A Conceptual Framework. Applied Bionics and Biomechanics. ISSN 1176-2322. 2022. doi: 10.1155/2022/3859629. Full text in Research Archive Show summary
Land registration authorities are frequently held accountable for the alleged mismanagement and manipulation of land records in various countries. Pakistan’s property records are especially vulnerable to falsification and corruption because of the country’s poverty. Different parties therefore claim varying degrees of authority over a specific piece of land. Given the fact that this data has been consolidated, it has become significantly more vulnerable to security threats. The goal of decentralized system research has been to increase the reliability of these systems. In order to fix the flaws of centralized systems, blockchain-based decentralized systems are currently in development. By using significant land record registration models as the basis for this research, we hope to create a proof-of-concept system or framework for future use. Pakistan’s land registration agency will benefit from our proposed conceptual framework. For the Pakistani government to implement a decentralized land record registry system, we propose a conceptual framework that outlines the essential components.
Hussain, Dostdar; Hussain, Israr; Ismail, Muhammad; Alabrah, Amerah; Sajid Ullah, Syed & Alaghbari, Hayat Mansoor (2022). A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition. Computational Intelligence and Neuroscience. ISSN 1687-5265. 2022. doi: 10.1155/2022/6538117. Full text in Research Archive Show summary
Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits’ production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements.
Hussain, Tahir; Hussain, Dostdar; Hussain, Israr; Alsalman, Hussain; Hussain, Saddam & Sajid Ullah, Syed [Show all 7 contributors for this article] (2022). Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems. Computational & Mathematical Methods in Medicine. ISSN 1748-670X. 2022. doi: 10.1155/2022/5137513. Full text in Research Archive Show summary
Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.
Ibrahim, Muhammad Junaid; Kainat, Jaweria; Alsalman, Hussain; Sajid Ullah, Syed; Al-Hadhrami, Suheer & Hussain, Saddam (2022). An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods. Applied Bionics and Biomechanics. ISSN 1176-2322. 2022. doi: 10.1155/2022/7931729. Full text in Research Archive Show summary
Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.
Hassan, Ch Anwar Ul; Hammad, Muhammad; Uddin, Mueen; Iqbal, Jawaid; Sahi, Jawad & Hussain, Saddam [Show all 7 contributors for this article] (2022). Optimizing the Performance of Data Warehouse by Query Cache Mechanism. IEEE Access. ISSN 2169-3536. 10, p. 13472–13480. doi: 10.1109/ACCESS.2022.3148131. Full text in Research Archive
Sridhar, V.; Ranga Rao, Rao; Vinay Kumar, Kumar; Mukred, Muaadh; Sajid Ullah, Syed & Alsalman, Hussain (2022). A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks. Mathematical Problems in Engineering. ISSN 1024-123X. 2022. doi: 10.1155/2022/6391678. Full text in Research Archive Show summary
Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.
Arif, Syed Muhammad; Bacha, Bakht Amin; Sajid Ullah, Syed; Hussain, Saddam & Haneef, Muhammad (2022). Tunable control of internet of things information hacking by application of the induced chiral atomic medium. Soft Computing - A Fusion of Foundations, Methodologies and Applications. ISSN 1432-7643. doi: 10.1007/s00500-022-06788-4. Show summary
The increasing demands of new technologies in the domain of Internet of Things (IoT) change the way of data transmission medium from air to light and is called quantum communication. For secure quantum communication among IoT devices, in the present article, we provide a novel quantum communication model for IoT devices. In our proposed model, the intensity variations of left/right circularly polarized beams through an induced chiral medium is investigated with time, having positive and negative phase shifts that create a time hole. This time hole has the ability to hide IoT information from outside hackers/attackers. At certain phases of the control fields and complex coefficients of the superposition states, a temporal time gap of about 13 μs is generated, which is modified to 30 μs with intensities of control fields and their detuning’s. This theoretical analysis may show new openings to control IoT information hacking and develop time cloak technology.
Hussain, Saddam; Sajid Ullah, Syed; Uddin, Mueen; Iqbal, Jawaid & Chen, Chin-Ling (2022). A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks. Sensors. ISSN 1424-8220. 22(3). doi: 10.3390/s22031072. Full text in Research Archive Show summary
WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it’s vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs’ success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird’s eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN’s communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.
Khan, Muhammad Zakir; Naseem, Rashid; Anwar, Aamir; Haq, Ijaz Ul; Alturki, Ahmad & Sajid Ullah, Syed [Show all 7 contributors for this article] (2022). A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System. Journal of Mathematics. ISSN 2314-4629. 2022. doi: 10.1155/2022/8640596. Full text in Research Archive Show summary
Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.

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Haque, Ehtisham Ul; Shah, Adil; Iqbal, Jawaid; Sajid Ullah, Syed; Alroobaea, Roobaea & Hussain, Saddam (2024). Correction to: A scalable blockchain based framework for efficient IoT data management using lightweight consensus (Scientific Reports, (2024), 14, 1, (7841), 10.1038/s41598-024-58578-7). Scientific Reports. ISSN 2045-2322. 14(1). doi: 10.1038/s41598-024-60003-y.
Arif, Syed Muhammad; Bacha, Bakht Amin; Sajid Ullah, Syed; Hussain, Saddam & Haneef, Muhammad (2023). Correction to: Tunable control of internet of things information hacking by application of the induced chiral atomic medium (Soft Computing, (2022), 26, 20, (10643-10650), 10.1007/s00500-022-06788-4). Soft Computing - A Fusion of Foundations, Methodologies and Applications. ISSN 1432-7643. doi: 10.1007/s00500-023-08107-x.

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Published Apr. 16, 2024 11:12 AM