Blockchain-Empowered Data Security and Privacy-Preserving Framework for Wearable Medical Devices A. Sasikumar, P. Selvakumari, P. Rajasekar, S. Wilson Prakash, Imran Javeed Settu Wearable Medical Device Security and Network Protection for Reliable Healthcare, 2025 The value of individual lives is anticipated to be enhanced by wearable medical devices. Secure sharing and protecting data privacy are two of the primary technological challenges that smart wearable medical devices must address. A centralized organization that verifies transactions and interaction permissions generally serves as the foundation for medical management devices. Consequently, it is possible to consider this centralized organization a single point of failure. We propose a novel blockchain-empowered wearable device secure system that protects the privacy of medical devices. The proposed framework enhances security and productivity by fusing smart contracts for data processing automation with blockchain technology's decentralized, transparent, and impenetrable features. Smart contracts can quickly identify vulnerable devices that have been compromised or represent a risk to the medical network. Therefore, establishing control over one's own devices protects the data and prevents malicious activity. privacy of the data owner.
Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System K. Priya, K. Priyadharshini, R. Santhana Krishnan, J. Relin Francis Raj, Imran Javeed Settu, A. Srinivasan Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025 Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.
Deep Learning Models for Acoustic Monitoring of the Himalayan Monal and Other Elusive Bird J. Ebens Nikshya, J. Relin Francis Raj, R. Santhana Krishnan, G.Vinoth Rajkumar, Imran Javeed Settu, S. Kavitha 4th International Conference on Sentiment Analysis and Deep Learning Icsadl 2025 Proceedings, 2025 Acoustic monitoring of rare and elusive bird species presents significant challenges due to the complexity of environmental noise and the variability in bird vocalizations. This study proposes a novel deep learning framework, BioAudio VisionNet (BAVNet), specifically designed for the acoustic identification and monitoring of the Himalayan Monal and other rare bird species. The performance of the proposed algorithm was evaluated against three established algorithms: Support Vector Machines with Acoustic Features (SVM-AF), Audio Spectrogram Transformer (ASF), and Deep Belief Networks (DBN). Simulation experiments were conducted using metrics such as accuracy versus the number of epochs, processing time versus accuracy, and spectrogram classification accuracy versus spectrogram resolution. The results demonstrated that BAVNet achieved a notable improvement in classification accuracy, computational efficiency, and adaptability to varying spectrogram resolutions when compared to existing methods. These findings highlight the potential of BAVNet for real-time biodiversity monitoring in challenging environments. Future work can explore the integration of additional contextual data, such as environmental factors, to further enhance system performance.
Next-Generation Rural Connectivity: Hybrid Beamforming in Low-Frequency Networks J.Relin Francis Raj, A.Alice Blessie, R.Santhana Krishnan, G.Vinoth Rajkumar, N.Eswara Prasath, Imran Javeed Settu Proceedings of the International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2025, 2025 This research investigates the implementation and evaluation of hybrid beamforming in rural low-frequency bands to enhance network performance in challenging terrains under varying weather conditions. The study leverages NS-3 simulations, Geographic Information System (GIS) data, and real-time weather information to model hybrid beamforming performance in rural environments. A modular massive MIMO base station is configured with user equipment (UEs) distributed across flat plains, hilly regions, and forested areas. The hybrid beamforming approach is compared with traditional beamforming and sectorized antennas based on key performance metrics, including Signal-to-Noise Ratio (SNR), Signal-to-Interference-plus-Noise Ratio (SINR), network throughput, Packet Delivery Ratio (PDR), and coverage area. The results show that hybrid beamforming significantly improves performance across all metrics. For instance, in flat plains under clear skies, the SNR for hybrid beamforming is 30 dB, compared to 25 dB for traditional beamforming and 20 dB for sectorized antennas. Similar improvements are observed for SINR, throughput, PDR, and coverage area. Hybrid beamforming also demonstrates superior robustness under adverse weather conditions, such as rainfall and high humidity, where it outperforms traditional beamforming and sectorized antennas in terms of coverage and throughput. Future work could focus on integrating machine learning techniques for adaptive beamforming and expanding simulations to multi-cell network scenarios, providing further optimization and scalability for rural wireless networks.
A Low Profile Two Port MIMO Antenna with Enhanced Isolation for V2X Communication Sarah Nara, B Poorna Chandra., N Manikanta., Beulah Jackson, S Imran Javeed. 2024 2nd World Conference on Communication and Computing Wconf 2024, 2024 This paper expresses the functionality and design of a flexible MIMO antenna to meet the increasing need for efficient V2X communication systems. The antenna's structural characteristics were carefully tuned using HFSS simulation. The antenna's resonance, impedance matching, and isolation capabilities were improved by adjusting the key geometric specifications, such as length, width, and diameter values. The examination of the S-parameters yielded valuable insights into the reflection and transmission properties of the antenna, whilst the investigation of VSWR highlighted its effectiveness in impedance matching. Furthermore, the gain plot model provided a clear understanding of the antenna's directional radiation pattern, which is essential for enhancing signal coverage and reception. The results of this study highlight the effectiveness of the suggested MIMO antenna design in satisfying the rigorous demands of V2X communication. The antenna achieves improved resonance, impedance matching, and radiation efficiency by carefully adjusting its structural characteristics. This contributes to the advancement of reliable communication systems for contemporary vehicle situations.
IoT based Smart Voice and Gesture Controlled Wheelchair C Kanmani Pappa, D Rajesh Kumar, D Preethi, M Manimara Boopathy, S Imran Javeed 5th International Conference on Electronics and Sustainable Communication Systems Icesc 2024 Proceedings, 2024 The voice and gesture-controlled wheelchair introduces a new solution for the mobility of physically challenge ones. With the help of both voice command control and gesture control the wheelchair helps the person to navigate easily trough their life with ease and precision. By combining the three modes of control that are manual, gesture and voice control, the user can easily interact with the wheelchair without any difficulty. All the three modes of commands will be given through the app with the help of mobile. The wheelchair can be connected through Bluetooth with the mobile and the inputs will be given from the mobile phone through the application that is already installed in the mobile phone. The commands will be given through the mobile phone and the command will be transferred with the help of Bluetooth module connected to microcontroller. The required command will be executed with the help of microcontroller which has a set of command codes uploaded in it.
MultimodNet: A Multimodal Deep Learning Model for COPD Staging based on Chest X-Ray and Pulmonary Function Tests A. Krishnaveni, G. Vinoth Rajkumar, J. Relin Francis Raj, R. Santhana Krishnan, S. Murali, Imran Javeed Settu 3rd International Conference on Automation Computing and Renewable Systems Icacrs 2024 Proceedings, 2024 Chronic Obstructive Pulmonary Disease (COPD) represents a major global health challenge, with early and accurate diagnosis being essential for improving patient outcomes. Traditional diagnostic approaches often rely on single-modality assessments, which may not fully capture the complexity of the disease. This research introduces MultimodNet, a deep learning framework that integrates both chest X-ray images and Pulmonary Function Test (PFT) data to provide a more comprehensive classification of COPD stages. By combining structural information from chest X-rays with functional lung metrics like FEV1, FVC, and the FEV1/FVC ratio, MultimodNet aims to enhance the accuracy and reliability of COPD diagnosis. The core strength of MultimodNet lies in its multimodal approach, which allows for the processing and fusion of diverse data types to overcome the limitations of using a single modality. This integration helps the model provide more accurate predictions regarding COPD progression, classifying patients into one of four stages: Initial, Progressive, Complicated, and Critical. These stages reflect the disease's clinical trajectory, from the onset of symptoms through to advanced stages where life-threatening complications may arise. By leveraging both imaging and clinical data, MultimodNet offers a powerful diagnostic tool that can assist healthcare providers in making early and precise decisions, leading to better treatment planning and improved patient care. This framework has the potential to not only enhance diagnostic workflows but also contribute to the broader field of AI-driven healthcare solutions, where multimodal data integration plays a key role in advancing disease detection and management.
Internet of Things (IoT) Technology: A Critical Component of Industry 4.0 Rachit Jain, Poonam Tiwari, Pooja Jain, R. Ramasamy, Javeed S. Imran, S. Udhayanan Advanced Iot Technologies and Applications in the Industry 4 0 Digital Economy, 2024 Industry 4.0, which is characterized by the integration of physical and digital systems, has revolutionized manufacturing and other sectors. The internet of things (IoT) technology, which enables the seamless connectivity and communication of devices, machines, and systems, is at the heart of this revolution. This research chapter discusses the major components, benefits, problems, and future prospects of IoT in Industry 4.0. Understanding the importance of IoT in this context allows industries to fully realize its promise to enhance efficiency, productivity, and innovation.
Image Enhancement in Fundus Images using Modified Frangi Vesselness Filter in comparison with Hessian and Gaussian Filter Nanmaran R, Vijai Meyyappan Moorthy, Srinath R, Ramasamy R, Imran Javeed S, Srimathi S Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024 The main aim of this work to develop image enhancement algorithm using modified vesselness filter for fundus image. Image enhancement using modified vesselness filter has been designed and compared with hessian and Gaussian filters. Performance of proposed algorithm is measured using high values of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC), and Naturalness Image Quality Evaluator (NIQE). Modified frangi vesselness filter provides PSNR = 41.31db, SSIM = 92.9%, NC = 23.25%, NIQE = 30.70%. Hessian filter provides PSNR = 41.37db, SSIM = 10.85%, NC = 18.65%, NIQE = 23.64%. Gaussian filter provides PSNR = 11.33db, SSIM = 95.23%, NC = 99.24%, NIQE = 6.29%. The designed filters were tested against various noises like Gaussian, salt and pepper, speckle noise. All three filters performed good against Gaussian noise over other two types of noises. Based on the simulation results modified vesselness filter performed well when compare other two filters. Keywords: Improved Filter Design, Frangi Vesselness Filter, Hessian Filter, Gaussian Filter, Medical Image Enhancement.
Efficient biometric recognition methodology using Guided Filtering and SIFT feature matching International Journal of Engineering and Technology Uae, 2018
Novel Isolation Enhanced Broadband MIMO Antenna Design for 5G Communications: Extended Mid-Microwave Band of 6 GHz to 13 GHz IJ Settu, D Muthukumaran, R Dileepan, M Pandiyarajan, S Aswath, ... 2026 6th International Conference on Trends in Material Science and … , 2026 2026
Advancing Mental Health Detection Using Machine Learning M Pandiyarajan, DJ Latha, IJ Settu, BVR Kumar, Y Kesava, MK TS 2026 International Conference on Computing, Electronics & Communications … , 2026 2026
Blockchain-Empowered Data Security and Privacy-Preserving Framework for Wearable Medical Devices A Sasikumar, P Selvakumari, P Rajasekar, SW Prakash, IJ Settu Wearable Medical Device Security and Network Protection for Reliable … , 2026 2026
Design of SHF Super Wideband Antenna for 5G and 6G mm-Wave Cognitive Radio Network Applications IJ Settu, B Jackson, A Sasikumar, R Nanmaran, M Pandiyarajan, ... Millimeter Wave and Terahertz Devices for 5G and 6G systems: Modern Design … , 2025 2025
Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System K Priya, K Priyadharshini, RS Krishnan, JRF Raj, IJ Settu, A Srinivasan 2025 International Conference on Intelligent Computing and Control Systems … , 2025 2025 Citations: 4
Deep Learning Models for Acoustic Monitoring of the Himalayan Monal and Other Elusive Bird JE Nikshya, JRF Raj, RS Krishnan, GV Rajkumar, IJ Settu, S Kavitha 2025 4th International Conference on Sentiment Analysis and Deep Learning … , 2025 2025 Citations: 3
Next-Generation Rural Connectivity: Hybrid Beamforming in Low-Frequency Networks JRF Raj, AA Blessie, RS Krishnan, GV Rajkumar, NE Prasath, IJ Settu 2025 International Conference on Multi-Agent Systems for Collaborative … , 2025 2025 Citations: 1
MultimodNet: A Multimodal Deep Learning Model for COPD Staging based on Chest X-Ray and Pulmonary Function Tests A Krishnaveni, GV Rajkumar, JRF Raj, RS Krishnan, S Murali, IJ Settu 2024 3rd International Conference on Automation, Computing and Renewable … , 2024 2024 Citations: 11
IoT based Smart Voice and Gesture Controlled Wheelchair CK Pappa, DR Kumar, D Preethi, MM Boopathy, SI Javeed 2024 5th International Conference on Electronics and Sustainable … , 2024 2024
A Low Profile Two Port MIMO Antenna with Enhanced Isolation for V2X Communication S Nara, BP Chandra, N Manikanta, B Jackson, SI Javeed 2024 2nd World Conference on Communication & Computing (WCONF), 1-5 , 2024 2024 Citations: 1
Image Enhancement in Fundus Images using Modified Frangi Vesselness Filter in comparison with Hessian and Gaussian Filter R Nanmaran, VM Moorthy, R Srinath, R Ramasamy, S Srimathi 2024 2nd International Conference on Networking and Communications (ICNWC), 1-11 , 2024 2024
4 Internet of Things R Ramasamy, JS Imran, S Udhayanan Advanced IoT Technologies and Applications in the Industry 4.0 Digital … , 2024 2024
Internet of Things (IoT) technology: A critical component of industry 4.0 R Jain, P Tiwari, P Jain, R Ramasamy, JS Imran, S Udhayanan Advanced IoT Technologies and Applications in the Industry 4.0 Digital … , 2024 2024 Citations: 5
Hybrid spectrum sensing based on adaptive threshold joint correlation and energy detection for cognitive radio network IJ Settu, GAA Mary CONFERENCE PROCEEDINGS ON 3RD INTERNATIONAL CONFERENCE ON ENGINEERING … , 2023 2023
A Dual Notch Band UWB Antenna for Local Area Cognitive Radio Network Applications IJSAA Mary G International Journal of Electrical and Electronics Research (IJEER) 11 (1 … , 2023 2023 Citations: 3
A 200 MHz to 850 MHz VHF/UHF Antenna for Local Area Cognitive Radio Networks Applications IJ Settu, AAM Gnanaraj 2023 Citations: 1
DNA sequence classification with improved performance of supervised classifiers using nature inspired algorithms S Aswath, CHMS Kumar, VH Deepthi, SI Javeed, SVN Rupesh 2022 2nd International Conference on Intelligent Technologies (CONIT), 1-7 , 2022 2022 Citations: 6
Efficient Biometric Recognition Methodology using Guided Filtering and SIFT Feature Matching RK Imran Javeed.S1*, Aanandha Saravanan2 International Journal of Engineering & Technology 7 , 2018 2018
Power Optimization of EPC Class-1 Gen-2 RFID Sensory Tags SI Javeed, B Venkatalakshmi Procedia Engineering 38, 373-378 , 2012 2012
MOST CITED SCHOLAR PUBLICATIONS
MultimodNet: A Multimodal Deep Learning Model for COPD Staging based on Chest X-Ray and Pulmonary Function Tests A Krishnaveni, GV Rajkumar, JRF Raj, RS Krishnan, S Murali, IJ Settu 2024 3rd International Conference on Automation, Computing and Renewable … , 2024 2024 Citations: 11
DNA sequence classification with improved performance of supervised classifiers using nature inspired algorithms S Aswath, CHMS Kumar, VH Deepthi, SI Javeed, SVN Rupesh 2022 2nd International Conference on Intelligent Technologies (CONIT), 1-7 , 2022 2022 Citations: 6
Internet of Things (IoT) technology: A critical component of industry 4.0 R Jain, P Tiwari, P Jain, R Ramasamy, JS Imran, S Udhayanan Advanced IoT Technologies and Applications in the Industry 4.0 Digital … , 2024 2024 Citations: 5
Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System K Priya, K Priyadharshini, RS Krishnan, JRF Raj, IJ Settu, A Srinivasan 2025 International Conference on Intelligent Computing and Control Systems … , 2025 2025 Citations: 4
Deep Learning Models for Acoustic Monitoring of the Himalayan Monal and Other Elusive Bird JE Nikshya, JRF Raj, RS Krishnan, GV Rajkumar, IJ Settu, S Kavitha 2025 4th International Conference on Sentiment Analysis and Deep Learning … , 2025 2025 Citations: 3
A Dual Notch Band UWB Antenna for Local Area Cognitive Radio Network Applications IJSAA Mary G International Journal of Electrical and Electronics Research (IJEER) 11 (1 … , 2023 2023 Citations: 3
Next-Generation Rural Connectivity: Hybrid Beamforming in Low-Frequency Networks JRF Raj, AA Blessie, RS Krishnan, GV Rajkumar, NE Prasath, IJ Settu 2025 International Conference on Multi-Agent Systems for Collaborative … , 2025 2025 Citations: 1
A Low Profile Two Port MIMO Antenna with Enhanced Isolation for V2X Communication S Nara, BP Chandra, N Manikanta, B Jackson, SI Javeed 2024 2nd World Conference on Communication & Computing (WCONF), 1-5 , 2024 2024 Citations: 1
A 200 MHz to 850 MHz VHF/UHF Antenna for Local Area Cognitive Radio Networks Applications IJ Settu, AAM Gnanaraj 2023 Citations: 1
Novel Isolation Enhanced Broadband MIMO Antenna Design for 5G Communications: Extended Mid-Microwave Band of 6 GHz to 13 GHz IJ Settu, D Muthukumaran, R Dileepan, M Pandiyarajan, S Aswath, ... 2026 6th International Conference on Trends in Material Science and … , 2026 2026
Advancing Mental Health Detection Using Machine Learning M Pandiyarajan, DJ Latha, IJ Settu, BVR Kumar, Y Kesava, MK TS 2026 International Conference on Computing, Electronics & Communications … , 2026 2026
Blockchain-Empowered Data Security and Privacy-Preserving Framework for Wearable Medical Devices A Sasikumar, P Selvakumari, P Rajasekar, SW Prakash, IJ Settu Wearable Medical Device Security and Network Protection for Reliable … , 2026 2026
Design of SHF Super Wideband Antenna for 5G and 6G mm-Wave Cognitive Radio Network Applications IJ Settu, B Jackson, A Sasikumar, R Nanmaran, M Pandiyarajan, ... Millimeter Wave and Terahertz Devices for 5G and 6G systems: Modern Design … , 2025 2025
IoT based Smart Voice and Gesture Controlled Wheelchair CK Pappa, DR Kumar, D Preethi, MM Boopathy, SI Javeed 2024 5th International Conference on Electronics and Sustainable … , 2024 2024
Image Enhancement in Fundus Images using Modified Frangi Vesselness Filter in comparison with Hessian and Gaussian Filter R Nanmaran, VM Moorthy, R Srinath, R Ramasamy, S Srimathi 2024 2nd International Conference on Networking and Communications (ICNWC), 1-11 , 2024 2024
4 Internet of Things R Ramasamy, JS Imran, S Udhayanan Advanced IoT Technologies and Applications in the Industry 4.0 Digital … , 2024 2024
Hybrid spectrum sensing based on adaptive threshold joint correlation and energy detection for cognitive radio network IJ Settu, GAA Mary CONFERENCE PROCEEDINGS ON 3RD INTERNATIONAL CONFERENCE ON ENGINEERING … , 2023 2023
Efficient Biometric Recognition Methodology using Guided Filtering and SIFT Feature Matching RK Imran Javeed.S1*, Aanandha Saravanan2 International Journal of Engineering & Technology 7 , 2018 2018
Power Optimization of EPC Class-1 Gen-2 RFID Sensory Tags SI Javeed, B Venkatalakshmi Procedia Engineering 38, 373-378 , 2012 2012
