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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kola, Sai Sindhu Kenchi, Harsha Rama, P. Angayarkanni, V. Kalaivani, J. Sivakumar, S. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India Srm Institute of Science and Technology Computing TechnologiesCollege of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Faculty of Engineering and Technology Srmist Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331529833

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based. Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting. © 2025 IEEE.

关键词： Face recognition

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system's capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system's superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management. © 2025 IEEE.

关键词： Environmental monitoring

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OMSACC-SGRAN: an implementation of hybrid Optimized Multi-Scale Atrous Convoluted CNN with Self Guided Residual Attention Network for fish species classification

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Multimedia Tools and Applications 2024年第39期83卷 87199-87235页

作者： M, Bhanumathi B, Arthi Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur Chennai603203 India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur Chennai603203 India

Visualizing the behavior of fish variants is the primary significance for obtaining biological insights in the marine ecological system. Several computer vision and machine learning-based approaches are introduced to classify the fish variants, but these require large data sets to provide high classification accuracy. Deep structured models handle these issues, but they need more attention during training time because some useful information is missed during the training process. It makes it possible to classify the fish variants inaccurately. Therefore, an automatic fish species classification system is developed to provide high accuracy and avoid misclassification results. Thus, this research work explores a new fish species classification strategy with the adoption of a hybrid deep learning technique. The acquired images are pre-processed using contrast-limited Adaptive Histogram Equalization (CLAHE) and histogram equalization methods for cleaning and increasing the quality of images. These images are fed to the hybrid classifier known as Optimized Multi-Scale Atrous Convoluted CNN with Self-Guided Residual Attention Network (OMSACC-SGRAN). Here, the parameters of both Multi-scale Atrous Convoluted CNN and Multi-scale Self Guided Residual Attention Network are tuned using a newly recommended Marine Predators-Forensics-based Investigation inspired Algorithm (MP-FBI). The experimental results proved that this approach achieve excellent results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fish

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Novel Homomorphic Encryption for Mitigating Impersonation Attack in Fog computing

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Intelligent Automation & Soft computing 2023年第2期35卷 2015-2027页

作者： V.Balaji P.Selvaraj Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge *** facil-itates the beneﬁts of the network edge with the maximized probability of offering interaction with the ***,the fog computing characteristics are suscep-tible to counteract the challenges of *** issues present with the Physical Layer Security(PLS)aspect in fog computing which included authentication,integrity,and conﬁdentiality has been considered as a reason for the potential issues leading to the security *** this work,the Octonion Algebra-inspired Non-Commutative Ring-based Fully Homomorphic Encryption Scheme(NCR-FHE)was proposed as a secrecy improvement technique to overcome the impersonation attack in cloud *** proposed approach was derived through the beneﬁts of Octonion algebra to facilitate the maximum security for big data-based *** major issues in the physical layer security which may potentially lead to the possible security issues were identiﬁ*** potential issues causing the impersonation attack in the Fog computing environment were identiﬁ*** proposed approach was compared with the existing encryption approaches and claimed as a robust approach to identify the impersonation attack for the fog and edge *** computation cost of the proposed NCR-FHE is identiﬁed to be signiﬁcantly reduced by 7.18%,8.64%,9.42%,and 10.36%in terms of communication overhead for varying packet sizes,when compared to the benchmarked ECDH-DH,LHPPS,BF-PHE and SHE-PABF schemes.

关键词： Fog computing physical layer security non-commutative ring-based fully homomorphic encryption impersonation attack

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YOLO-based Traffic Signal Optimization for Intelligent Traffic Flow Management 8

YOLO-based Traffic Signal Optimization for Intelligent Traff...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Joesam Dinesh, C. Shrinidhi, S. Amaran, Sibi Sree Kumar, K. Karthikeyan, U. Srm Institute of Science and Technology Dept of Computing Technologies Kattankulathur India Dept of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology Kattankulathur SRM Nagar Tamilnadu Chennai603203 India

ISBN: (纸本)9798350376425

As cities grow in population and the number of vehicles increases, traffic congestion has become a major problem. Traffic jams lead to delays, driver stress, higher fuel consumption, and increased air pollution. Megacities are particularly affected. To manage traffic effectively, it's essential to calculate road traffic density in real-time. Optimizing traffic control is crucial for this. The system uses live images from traffic cameras and AI-based image processing to measure vehicle density and adjust traffic signals accordingly, reducing congestion, speeding up transit, and lowering pollution. © 2024 IEEE.

关键词： Traffic congestion

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depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

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Intelligent Automation & Soft computing 2023年第7期37卷 1145-1161页

作者： H.Shalma P.Selvaraj Department of Computing Technologies Faculty of Engineering and TechnologySchool of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203Tamil NaduIndia

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

关键词： depth maps occlusion detection reconstruction refined disparity map

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Enhanced Privacy in Data Aggregation with Secret Sharing Techniques 1

Enhanced Privacy in Data Aggregation with Secret Sharing Tec...

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1st IEEE International Conference on computing and Data Science, ICCDS 2024

作者： Yugandhar, A.C.K. Hemanth, Mandyam Gnanavel, S. SRM Institute of Science and Technology Faculty of Engineering and Technology Dept of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350365337

The effectiveness of wireless sensor networks is greatly impacted by wireless spoofing attacks, which are simple to initiate. The difficult issues in Wireless Sensor Networks are locating many adversaries and eliminating them, identifying spoofing attackers, and estimating the number of attackers. The spoofing attackers are located and detected using the clustering approach. This method is unable to correctly identify the attackers. Intrusion Detection System (IDS) is suggested as a solution to this issue in order to identify spoofing attackers. In order to detect the misbehaving nodes in a wireless sensor network, VANET IDS monitors node behavior in their car, such as packet transfer. The simulation results clearly show that the proposed strategy effectively and consistently detects accidents in wireless sensor networks. © 2024 IEEE.

关键词： Cryptography

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Advanced Pedestrian Detection in Low Visibility Scenarios Using UltraLytics YOLOv8 and Kalman Filtering 5

Advanced Pedestrian Detection in Low Visibility Scenarios Us...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pattem, Sanjay Kumar Reddy Rajagopal, Manjula Nirujogi, Nandan SRM Institute of Science and Technology Computing Technologies Chennai India SRM Institute of Science and Technology Computing Technologies Faculty of Engineering and Technology Chennai India

ISBN: (纸本)9798331530013

Nighttime detection of pedestrians brings important problems such as low light conditions, limitations of sensors, and environmental noise. To tackle this problem, in this study, we propose a novel visual-radar data fusion to enhance a pedestrian detection system using the UltraLytics YOLOv8 deep learning model together with Kalman Filtering technique. The system is based on a Low-Light Vision Infrared Paired (LLVIP) dataset that includes infrared vision data to attenuate the performance decrement of a pedestrian detector in the dark. The combination of infrared cameras and millimeter-wave radar achieves a detection rate of 97.6%, in which UltraLytics YOLOv8 exhibits excellent object recognition performance by generating accurate spatial & category information from the infrared image;complementary distance and velocity data are obtained by millimeter-wave radar. It brings Kalman Filtering to track the radar-detected pedestrian targets, making the system more robust by predicting target and noise reduction. It was capable of fusing infrared and radar data at decision-level to make the system provide very precise position accurately in even nighttime situations. The experimental results show that the proposed model has improved capability of detection accuracy, robustness, and response time in comparison with the single-sensor system, which is suitable for practical autonomous driving scenarios. © 2024 IEEE.

关键词： Kalman filters

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Artificial Intelligence and Mobile Apps Support Intelligent Healthcare Systems for Mental Health Services

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile technologies 2024年第20期18卷 157-168页

作者： Jeya, R. Karim, Hezerul Abdul Mansor, Sarina Binti Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Faculty of Engineering Multimedia University Cyberjaya Malaysia

Patients and healthcare practitioners have commended the systems’ effectiveness, ease, and user-friendliness in several situations. Utilizing cutting-edge ideas and methods from the multidisciplinary domains of electricity, computing, medical engineering, and medicine, mobile healthcare (m-health) technology advances these professions’ contributions to healthcare systems. The monitoring and delivery of healthcare interventions are becoming increasingly dependent on mobile phones. Because of their sophisticated processing functions, improved preferences, and wide range of capabilities, they are frequently referred to as pocket computers. Their advanced sensors and intricate software programmers increase the viability and innovation of m-health solutions. To design the m-health application, the design science research methodology (DSRM) framework was used. Additionally, the architecture for connecting the hospital information system and mobile device network together was established. Additionally, a few exemplary intelligent healthcare applications are examined to demonstrate how data analytics and mobile computing can be used to improve the quality of healthcare services. © 2024 by the authors of this article.

关键词： Electronic health record

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Mobile Health Service Adoption: Reducing the Digital Gap and Age’s Moderating Influence

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile technologies 2024年第17期18卷 139-153页

作者： Muruganantham, B. Karim, Hezerul Abdul Mansor, Sarina Binti Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Faculty of Engineering Multimedia University Cyberjaya Malaysia

In the past few years, mobile health services (MHS)—a growing topic in the healthcare sector—have received increasing attention. technologies related to mobile health, or m-health, have several advantages for people, businesses, and medical professionals. Indeed, using m-health can help older people become more proactive patients and lessen the strain on health systems and financial resources. However, the potentially revolutionary impact of m-health is constrained since a large number of senior citizens oppose its adoption, which creates an age-based digital divide. Few studies have examined how public consumers of MHS adopt MHS, even though prior study on this topic is very substantial. We discover that user attitudes towards MHS are positively impacted by perceived benefits and usefulness, perceived barriers, and external cues. Perceived service availability also has a major impact on perceived benefits and usefulness, which, when combined with attitude, directly increases intention. Additionally, we discover that the MHS’s utilization intent has moderating effects. The final section discusses the implications for mobile health marketing. The results indicate that perceived ease of use, vulnerability, and severity are more significant factors for middle-aged and older users to use MHS. The moderator analysis confirmed that different age groups have specific moderating effects on the adoption of mobile health services. © 2024 by the authors of this article.

关键词： Electronic health record

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