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

作者： Shakila, M. Govindaram, Anitha Madhumitha, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350376425

The widespread adoption of online exams has highlighted the need for robust security and integrity measures. This research proposes a scalable architecture that integrates blockchain technology to address these challenges. The proposed system incorporates a multi-layered architecture, including an application layer, blockchain layer, and user layer. Smart contracts automate key operations, ensuring transparency and trust. Encryption techniques and multi-factor authentication safeguard sensitive data. Performance indicators are evaluated to assess the system's scalability and reliability under varying loads. The integration of blockchain enhances the legitimacy of online assessments, fostering trust between students and academic institutions. This research demonstrates the transformative potential of blockchain technology in revolutionizing online examinations. By improving security, integrity, and scalability, this framework can create a more reliable and trustworthy learning environment. Future research will focus on further enhancing the framework and exploring its applications in diverse educational settings. © 2024 IEEE.

关键词： Blockchain

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A CNN Transit Model for Object Detection using YOLOv5 2

A CNN Transit Model for Object Detection using YOLOv5

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Natarajan, Yuvaraj Afra, J. Buvani Sankari, V. Gayathri, V. Malingan, Navaneeth KPR Institute of Engineering and Technology Department of Computer Science Engineering Coimbatore India Nunnari Labs Private Limited Coimbatore India

ISBN: (纸本)9798350327533

The rapid urbanization and burgeoning populations in cities worldwide have led to an increased demand for efficient and sustainable urban transit systems. In India, the pace of growth in the two-wheeler fleet is 30 times greater than the rate of growth in the population. The chance of mortality is 3.5 times higher for riders who don't wear helmets than for those who do. In view of this, developed an AI-based model for object detection to guarantee public safety and enable real-time monitoring of traffic law violations. The system uses a camera to capture images of bikes carrying three or more people who are not wearing helmets as well as to recognize the license plates of bikes. A CNN Transit model for traffic networks have been proposed. The model utilizes the Yolov5 backbone layer, which is fine-tuned by increasing hyperparameters. This results in the accuracy of 86 % and a significant reduction in training time, which enhances the model's performance. By addition of PANet and FPN to the Yolov5 Neck layer has improved the detection of far and tiny objects in traffic and roadways. This study emphasizes the potential of AI-ML technology to address urban transportation difficulties and foster a culture of safety by detecting objects. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Transit Feature Pyramid Network (FPN) Object Detection Path Aggregation Network (PANet) Traffic law Violations Yolo v5

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Soft kind of Computing Based Least Resource Data finding which is Used for Analysis purpose 4

Soft kind of Computing Based Least Resource Data finding whi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Rani, L. Pattathu Surendran, S. Monica, D. Kumar, V. Senthil Rajaram, Gnanajeyaraman Dwibedi, Rajat Kumar Simats University Saveetha School of Engineering Chennai India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Aarupadai Veedu Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

Deep learning (DL) approaches are utilized across three distinct contexts to showcase their applicability in commercial environments with restricted data for training. This study showcases the application of unsupervised DL with autoencoders that vary & illustrates how federation learning serves as a successful collaboration approach for machine learning (ML) that safeguards confidentiality of data. © 2024 IEEE.

关键词： Federated learning

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Towards Precise Peach Disease Detection: Leveraging PCNN with Enhanced Activation and AFHOA Fine-Tuning

Towards Precise Peach Disease Detection: Leveraging PCNN wit...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Anitha, Cuddapah Mahaveerakannan, R. Govindaraju, Sakthi Thendral, Tamil M. Sunkesula, Haseena Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Rajampet Andhra Pradesh Annamayya India

ISBN: (纸本)9798350374933

Many innovations in farming and manufacturing have helped the world's seven billion inhabitants get the food they need. Diseases and pests provide the greatest risk to food products because they reduce both the number besides quality of food. Various methodical methods have been devised to enhance food production while also reducing the risk of pests and illnesses affecting plants and fruits. The diagnosis of diseases is still dependent on human skill and manual labor when using these techniques. These past ten years have seen widespread usage of AI for the automation of various tasks, including harvesting in agriculture. Processing photos and identifying things using Machine Learning procedures is gaining popularity. We can automate harvesting by identifying plant diseases using Machine Learning algorithms;this will allow us to grow more food while cutting down on crop losses. Using the parallel convolution neural network (PCNN) suggested in this paper to detect peach disease traits-using the improved elu activation function rather than the traditional ReLu activation function-we first create an image denoising procedure to mitigate the impact of the complicated background on the images. Next, the classification accuracy is enhanced by fine-tuning the hyper-parameters of PCNN using the Archerfish hunting optimizer (AFHOA) model. This method proved effective for peach disease diagnosis, as evidenced by the results of 25513 photos. The greatest detection accuracy for black spot, peach was almost 92% to 96% accordingly. © 2024 IEEE.

关键词： Fruits

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The Performance Analysis of Federated Learning Methods for IoT with Big Data 18

The Performance Analysis of Federated Learning Methods for I...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Govindaram, Anitha Jegatheesan, A. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamilnadu India

ISBN: (纸本)9789380544519

ML that creates a global framework by gathering knowledge from a number of different dispersed edge clients. FL allows on-device training, keeps client information in private, and updates the frameworks. FL approaches presume computational capabilities at each edge-device/client, which may not always be the case. Due to its inherent distributed architecture, FL has the features that make it a good fit for IoT networks. Though, there are a few obstacles that are specific to FL, the maximum significant is training across significantly heterogeneous data sets on IoT devices. Numerous new studies contended to minimize consequences of diversity but obviously, the efficacy of potential resolutions has concluded researched or enumerated. This is because the homogeneity of devices and designs in the complicated Internet of Things (IoT) networks ultimately affects the FL learning process and produces conventional FL inappropriate to be straightforwardly organized. This research aimed to provide an introduction to FL, as well as a thorough examination of the problem statements and developing issues, especially those that arise when attempting to implement FL in heterogeneous IoT contexts using ML and DL techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Aggregation Deep learning Federated learning heterogeneity Machine learning

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Wearable Sensor-Based Gait Analysis: Advanced Feature Extraction Techniques 2

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Karthik Salian, Preethi K. Ranjith, H.D. Salian, Supriya Jayashree Shetty, K. Annapoorneshwari NMAM Institute of Technology Udupi Nitte India NMAM Institute of Technology Udupi Nitte India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Mangalore India

ISBN: (纸本)9798350383867

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-depth analysis, enabling effective feature extraction for comprehensive gait assessment. © 2024 IEEE.

关键词： Accelerometers

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Parkinson's Disease Diagnosis from Patients Speech Analysis 9

Parkinson's Disease Diagnosis from Patients Speech Analysis

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Harshitha Reddy, T. Aryagopal Sai Jagadeesh, Dumpa Venkata Anand, M. Pati, Peeta Basa Bhanu Prakash, K.N. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science And Engineering Bengaluru India A∗star Bioinformatics Institute Singapore

ISBN: (纸本)9798350394474

Parkinson's disease is a neurological disorder mostly observed in the population over the age of 60 years. Over 8.5 million people world-wide are affected by it. Early diagnosis of the disease helps take preventive and curative measures to enable the affected individuals to lead a normal life. Quality of speech is one of the key indicators for identification of the *** this study, we investigate the effectiveness of multiple machine learning algorithms on various speech related attributes to determine the existence of this disease. Additionally, we identify the crucial features which impact the model's functioning. The study employs Explainable AI (XAI) to investigate the influence of voice features on the research outcomes. The experimental findings showcase a diagnostic accuracy of 95% using Random Forest & XGBoost classifiers. We, using SHAP, identify that 6 features out of a set of 24 provide the best efficiency of diagnosis. © 2024 IEEE.

关键词： Support vector machines

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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A Comparative Assessment of the Contemporary Models for Automatic Short Answer Grading 6th

A Comparative Assessment of the Contemporary Models for Auto...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Haldar, Subhajit Sengupta, Souvik Das, Asit Kumar Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur India Aliah University Kolkata India

ISBN: (纸本)9789819780891

Automated Short Answer Grading (ASAG) is a popular task in education technology that automates the assessment of student-written short answers by leveraging computational techniques. Although ASAG has been investigated for many years, recent advancement in machine learning and deep learning has ignited new research in this field. In the past, researchers have come up with different approaches that can be broadly classified into two categories—handcrafted feature-based traditional approaches and deep-learning-based approaches. In recent times, transformer-based word embedding has revolutionized all the fields of NLP applications, including ASAG. This chapter aims to compare the performance of various methodologies employed in ASAG with particular attention to comparing feature extraction techniques, machine learning and deep learning models employed, the dataset used, and the assessment measures applied. The review of the literature reveals that combining transformer-based text embedding with handcrafted features is more effective than using conventional handcrafted features and deep learning features in both regression and classification tasks for ASAG. This finding is validated through an empirical study performed to evaluate and compare the efficiency of different handcrafted and semantic features on the ASAG task with a common dataset and two baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Android Malware Detection and Family Classification using Permission Features with Intrusion Awareness 21

Android Malware Detection and Family Classification using Pe...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Pardhi, Praful R. Kumar Rout, Jitendra Bais, Vanshika V. Pawar, Kapil V. Yadav, Mamta R. Gade, Harsh C. KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Computer Science & Engg. Shri Ramdeobab College of Engineering & Management School of Computer Engineering Nagpur India National Institute of Technology Department of Computer Science & Engineering Raipur India Shri Ramdeobaba College of Engineering & Management Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9798350358230

The relentless evolution and increasing intricacy of malware represent a formidable challenge to cybersecurity professionals. The presence of malware within a system poses a grave threat, encompassing a spectrum of potential calamities, from the theft of personal information to the sabotage of devices and networks. Therefore, it becomes imperative to devise solutions for the prompt and adaptive detection and classification of malware. This paper delves into the multifarious methods and techniques employed in the domain of Android malware detection and family categorization, with particular emphasis on identifying the permissions that could potentially lead to system intrusion. It also issues a warning message pertaining to the hazardous permissions that could potentially disrupt the system. By empowering users with this crucial information, the proposed system enable them to make informed decisions and take proactive measures to safeguard their systems. In a bid to ascertain the efficacy of our methods, rigorous testing of the dataset using a diverse range of machine learning models was conducted. The results were remarkable, with random forest for the identification of malware, adaboost for classification of family and isolation forest for identifying the permissions that can cause intrusion in the system. © 2023 IEEE.

关键词： Machine learning

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