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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Ascribing Machine Learning Classifiers to diagnose the attacks of Alternaria solani on Leaves of Solanum tuberosum 2

Ascribing Machine Learning Classifiers to diagnose the attac...

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2nd International Conference on computational Systems and Communication, ICCSC 2023

作者： Dutta, Anurag Kumar, Pijush Kanti De, Ankita Kumar, Padmanavan Dwivedi, Shubhangi Harshith, John Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore Calcutta India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Odisha Bhubaneswar India School of Mathematical and Statistical Science Indian Institute of Technology Himachal Pradesh Mandi India Vellore Institute of Technology Department of Computer Science Tamil Nadu Vellore India

ISBN: (纸本)9781665493932

Following the recent advances in technology, came advanced computational domains like, Internet of Things, Machine Learning, Artificial Intelligence, Data science, and many more. These fields really tend to help mankind a lot. In this work, we would make use of Machine Learning aspects to perform prediction of diseases in plant. Specifically, spot the Early Blight Disease in the Potato Leaves. The potato plant, Solanum tuberosum, is a significant crop that is grown all over the world and generates large quantities of tubers that are a good source of nutrients. The potato has many medicinal benefits in addition to being a common staple diet. When the fluid out from tubers is consumed in moderation, it can treat gastric ulcers and relieve inflammation and acidity. Two harmful potato diseases, late blight and early blight, are pervasive. Everywhere potatoes are cultivated, both are present. The labels 'Early' and 'Late' allude to the relative timing of their field emergence, however both disorders might manifest simultaneously. In this work, we would focus on Early Blight. The fungus Alternaria solani, that can infect potatoes, tomatoes, several species of the potato genus, and some mustards, is the cause of early blight of potatoes. Young, actively growing plants are rarely impacted by this disease, commonly known as target spot. It first appears on elder leaves. Warm temperatures and heavy humidity foster Early Blight. This disease affects the tuber symptomized by dark, rounded to irregular dots being developed on the tuber. As the disease develops, the flesh of the potatoes commonly becomes water-soaked yellow to greenish yellow. For this work, we have collected a set of nearly 1000 samples of Early Blight affected Potato Leaves. Using that, we have modelled a Machine Learning Classification Paradigm that could potentially predict the occurrence of Early Blight Disease making using of classical classifier algorithms. Medical science have advanced to a great height.

关键词： Machine learning

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Recent Innovations and Improvements in Remote Heart Rate and Heart Disease Measuring Methods Using RGB Camera

Recent Innovations and Improvements in Remote Heart Rate and...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Balaraman, Kalaiselvi Claret, S. P. Angelin College of Science and Humanities Department of Computer Science SRM Institute of Science and Technology Chennai Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Chennai Kattankulathur India

ISBN: (纸本)9798350333244

The Heart Rate (HR) is the most significant preliminary health parameter and a vital indication of people's physiological condition. Thus, it's crucial to monitor periodically. During a given period, the heart beats at a specified rate. The rate of heartbeat is called Heart Rate. Accurate HR is essential to diagnosing and predicting heart failures and cardiovascular diseases. In recent years several innovative approaches for retrieving heart rate using low-cost digital cameras and image processing have been published by researchers. The ubiquitous availability of digital cameras paves the way for several innovative and low-cost vital sign monitoring applications. The review was conducted on a remote HR measuring method, such that continuous monitoring of HRV, ECG-based, and rPPG-based monitoring methods and working principles were analyzed. This study specifically focuses on the recent enhancement and advancement of non-contact-based heart rate estimation, classifies existing Photoplethysmography (PPG) techniques, and describes each step required in rPPG classification. This study identified many authors' most effective methods for a higher accuracy ratio. The professionals can apply the evaluated and categorized rPPG classification algorithms to enhance some aspects of a newly constructed rPPG algorithm to improve the heart rate accuracy most suited to their specific needs. The rPPG-based non-contact HR measurement will suit the current COVID-19 environment in a clinical trial. © 2023 IEEE.

关键词： Electrocardiograms

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Implantable Smart Devices for Remote Health Monitoring to Detect Hypo/Hyperglycaemia

Implantable Smart Devices for Remote Health Monitoring to De...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Palaniappan, Ramya Siva, R. Surendran, Suseendran Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Computer Science Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9781665474511

Hypoglycemia / Hyperglycemia is a long-term metabolic disorder in which the Blood Glucose (BG) level of the patient is low or high than the standard range. High blood glucose level if untreated can cause heart disease, stroke, blindness, kidney failure and amputation of toes, feet or legs. The Internet of Medical Things(IoMT) is the amalgamation of the information from medical devices and medical software applications that connects healthcare IT systems seamlessly. Remote Health Monitoring System (RHMS) is the process of making use of the technology for providing medical services to the patients in non-clinical environment. In situations where the patients cannot reach the clinical environment, RHMS when incorporated will aid the healthcare professionals to deliver personalized treatment for the patients. In this study, IoMT enabled Continuous Glucose Monitoring (CGM) system enables continuous automated monitoring of blood glucose(BG) levels of the patients at regular intervals. This reduces the burden of measuring the BG levels and recording the results manually for further follow up. This continuous monitoring environment can send alerts to the patients and or to the clinicians when the BG levels are out of the normal range. This CGM system will be beneficial for the clinicians to alert Individuals about the upcoming health complications and recommend quality lifestyle for them to stay healthy. Yellow Springs Instrument's (YSI) BG readings are used to compare the proposed CGM system's performance in terms of Mean Absolute Relative Difference (MARD). Additionally, the challenges and threats associated with constructing the IoMT ecosystem were highlighted, as well as potential solutions. © 2023 IEEE.

关键词： Blood

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A Machine Learning Model: Student Verification and Job Placement with Gradient Boosting Machines 15

A Machine Learning Model: Student Verification and Job Place...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Sakthivelan, R.G. Subha, S. Sree Gopirajan, P.V. Thandalam Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India

ISBN: (纸本)9798331300579

Every year, millions of students graduate, making it difficult to track and process their data. The One Point Student Verification Machine learning Model (OSVMLM) collects rich data on students, including biometric information, secondary education details, list of degrees completed, and detailed information on their last or current degree. In addition, the student's fingerprint is also collected and stored. The government's Aadhar database is used to verify the authenticity of the student. To ease the recruiter, the details collected by OSVMLM were assigned with ranks, information will be sent to the candidates autonomously based on the current job description. This makes it easy for students to identify and apply for available positions. OSVMLM application is built on Ionic, MongoDB, NodeJS and Gradient boosting machines (GBMs). OSVMLM is immensely useful for recruiters, as it makes data collection and processing effortless. This increases the visibility of various jobs open in different domains and suggests suitable job description to the job seekers. The key difference between OSVMLM and other platforms is that it is not limited to institutions and is accessible to all. This saves a lot of time for recruiters during initial screening. This proposed OSVMLM was validated with the real-time students dataset and achieved 91% prediction accuracy. © Grenze Scientific Society, 2024.

关键词： Students

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Path Planning Solution for Intelligent Robots Using Ex*RRT Algorithm in Disaster Relief 5

Path Planning Solution for Intelligent Robots Using Ex*RRT A...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Hariprasath, V.Y. Mukesh, S. Aruna, T.N. Gayana, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Tiruchirappalli India KGiSL Institute of Technology Department of Information Technology Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350351378

Unmanned Ground Robots (UGVs) are increasingly becoming vital tools in disaster relief operations, offering a means to navigate through hazardous environments where human intervention may be risky or impossible. These intelligent robots are designed to perform a variety of tasks, such as searching for survivors, delivering supplies, and assessing damage. However, the effectiveness of UGVs in such critical missions largely depends on their ability to navigate complex and unpredictable terrains, a challenge that necessitates advanced path-planning strategies. The paper introduces a novel approach to path planning for UGVs in disaster relief scenarios. This approach combines the strengths of advanced rapidly Rapidly-exploring Random Trees (RRT) algorithms with the capabilities of YOLOv4, a state-of-the-art technique for object detection, specifically tailored here for human detection. RRT algorithms are known for their efficiency in exploring complex spaces, making them ideal for navigating through the unpredictable terrains typical of disaster zones. Meanwhile, YOLOv4 enhances the UGVs' ability to detect and respond to human presence, a critical requirement in rescue operations. The UGVs equipped with this novel path-planning strategy were able to navigate safely and efficiently, adapting to the dynamic nature of the environment. More importantly, the accuracy in detecting human presence was significantly improved, a crucial factor in ensuring that aid and rescue efforts are directed where they are most needed. By enhancing the path planning capabilities of UGVs with advanced algorithms and human detection technology, this approach holds the potential to revolutionize disaster response operations, making them more efficient and impactful, ultimately leading to faster and more effective aid delivery to those in dire need. © 2024 IEEE.

关键词： Motion planning

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Energy Efficient mathematical Model for Cyber-Physical Systems: A Model for Industrial Internet of Things

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SN computer science 2023年第5期4卷 1-7页

作者： Veerachamy, Ramachandran Rao, Ganga Rama Koteswara Arivanantham, Vishnu Priya Kuppusamy, Sangeetha Velayutham, Priya Govindaraj, Rajeshkumar Department of Computer Science and Engineering GITAM School of Computing GITAM Deemed to be University Bengaluru 561203 India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 520002 India Department of Computational Intelligence School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal 637018 India Department of Information Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamagalam 638401 India

Proliferated usage of computer devices, smart objects, and electronics significantly impacts the atmosphere, which is inevitable. The existing context has engineered schemes, or Cyber-Physical Systems (CPS), that integrate physical and computation components. The extensive usage of devices has utilized more energy, creating complexity in environmental handling. The problem is improving an automotive function’s reliability, application, or functionality with time and energy constraints. By applying smart technologies, namely Deep Learning (DL) and Machine Learning (ML) in CPS and the Internet of Things (IoT), enduring the utilization of these intensive DL-based approaches has a negative effect on resource utilization. The proposed Energy Efficient mathematical Model (EEMM) is designed with a mathematical model for IIoT, which guides in solving the drawback of the existing system. The mathematical model integrated CPS-based proposed EEMM has outperformed the existing state-of-art technique. The proposed EEMM attains minimal Energy Consumption (EC) of 521 Jules and the highest reliability rate of 0.987. The proposed EEMM in this research has attained minimal energy usage of 0.987 i for 100 tasks, which is minimal than other existing techniques. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cyber-attacks Energy IIoT IoT mathematical model Network Smart objects

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Enhanced Diagnosis of Schizophrenia Using Integrated Node Embedding and Causal Discovery on Imbalanced fMRI Data 10

Enhanced Diagnosis of Schizophrenia Using Integrated Node Em...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Sabzevar, Sina Masoudnia, Saeed Araabi, Babak N Nazemzadeh, MohammadReza Tavakoli, Hosna University of Tehran Department of Electrical and Computer Engineering Tehran Iran Monash University Department of Neuroscience Melbourne Australia Institute of Cognitive Science Studies Computational and Artificial Intelligence Department Tehran Iran

ISBN: (纸本)9798331532543

This study introduces a novel diagnostic method for schizophrenia using causal discovery and node embedding techniques on resting-state fMRI data. Data from 148 subjects (27 schizophrenia patients, 121 healthy controls) were analyzed across 116 brain regions, applying several causal discovery techniques to assess directional connectivity and uncover schizophrenia-related patterns. By combining advanced causal discovery with node embedding techniques and a rigorous data-balancing pipeline, our method improves sensitivity to subtle network variations critical for schizophrenia diagnosis. Our approach addresses key challenges in the literature: the difficulty of extracting effective neurobiological markers from noisy and complex fMRI data, the limited interpretability of connectivity networks, and the imbalance in datasets that often skews classification performance. Experimental results demonstrate the pipeline's effectiveness in overcoming imbalanced data challenges and extracting features from noisy and complex data, with Regression Dynamic Causal Modeling (RDCM) achieving the highest classification metrics, including 90.06% accuracy and 89.09% sensitivity. This method identified 15 brain regions with strong diagnostic potential, offering improved sensitivity and specificity for schizophrenia diagnosis Compared to contemporary domain techniques. © 2024 IEEE.

关键词： Diseases

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COVID-19 Classification using CNN, Inception V3 and Transfer Learning

COVID-19 Classification using CNN, Inception V3 and Transfer...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Mohammed Iqbal, M. Tamilmani, G. Tej Pavan, Caserker Hema Sri Pranav, Pendyala VIT-AP University School of Computer Science and Engineering Amaravati India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The outbreak of the COVID-19 pandemic has posed unprecedented challenges for global healthcare systems, neces-sitating accurate and rapid diagnostic solutions. The suggested method utilizes the capabilities of Convolutional Neural Networks (CNN) and the Inception V3 module to provide a novel deep learning strategy for COVID-19 identification. The capacity of the Inception V3 architecture to extract nuanced features from challenging medical imaging data is famous. This work evaluates the performance of the CNN-Inception V3 model in differentiating COVID-19 instances from other respiratory illnesses, such as pneumonia, fibrosis, tuberculosis, and normal lung scans, through a thorough examination employing a broad dataset of chest X-ray and CT images. Important parameters like accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are used to assess the model's performance. The study also explores the interpretability of CNN-Inception V3 model predictions, providing information on possible clinical uses. The results of this study add to the corpus of research on deep learning-based COVID-19 diagnostics and show the value of cutting-edge technology in the fight against infectious diseases. © 2023 IEEE.

关键词： CNN classification COVID-19 deep learning fibrosis Inception V3 pneumo-nia tuberculosis X-ray

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Comparative Analysis of Diverse Classification Algorithms of Machine Learning by Using Various Quality Metrics 5

Comparative Analysis of Diverse Classification Algorithms of...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Kathiresan, V. Dinesh, G. Sarveshwaran, Velliangiri Jayakanth, J.J. Kiruthika, M. Remu, Yuvaraj GITAM University Dept. of Computer Science and Engineering Bengaluru Karnataka India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350338287

Machine learning classification algorithms and their applications are becoming increasingly popular in today's era of information science. Choosing an algorithm that is appropriate for the problem and application is a challenging task. The quality of the prediction model is significantly influenced by selecting the right algorithm. The purpose of the study and comparative analysis highlights the benefits and drawbacks of various classification algorithms from all perspectives. The Machine Learning (ML) based classification algorithms such as Logistic Regression (LR), K-Nearest Neighbour(KNN), DT (Decision Tree), SVM (Support Vector Machine), and Naïve Bayes (NB) are assessed for comparative analysis. These algorithms were tested and analysed using various datasets acquired and used from the UCIML repository. Algorithms are evaluated using well-established effective measures for accuracy, recall, and precision. A variety of datasets are used to monitor, measure, and compare each algorithm's performance in terms of metrics. Each algorithm's effectiveness and efficacy are analysed and compared from all aspects. Based on overall performance, the algorithms are ranked in the following order: logistic regression, decision tree, SVM, KNN, and Nave Bayes. © 2023 IEEE.

关键词： Decision trees

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