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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Usha, V. Abhinash, Nalagarla Chiru Sathya, V. Kujani, T. Rao, P. S. Omkara Eswara Kishore, Koneti Leela Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Dept.of Cse Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept Of Computer Science And Business Systems Chennai India

ISBN: (纸本)9798350352689

Transportation operations in sparsely populated or rural areas are planned and organized in an efficient manner. The objective is to maximize the efficiency of transport service scheduling in order to cater to the movement requirements of the inhabitants in these regions. The aim is to optimize the functionality and efficiency of transportation services in rural regions by coordinating the available vehicles in a manner that satisfies the needs and preferences of the local populace while also taking into account logistical limitations. The implementation of machine learning incorporates a theoretical and data-driven strategy to enhance the transit timetable decision-making process. A dataset including vehicle capacities, available vehicle counts, and desired travel hours for people is essential to the project code. This project use unsupervised machine learning methods since the data does not have any labels. Clustering is a popular unsupervised machine learning technique because it groups similar data into meaningful categories. Among the clustering techniques that were used were K-Medoids, DBSCAN, Mini-Batch K-Means, and K-Means. Hierarchical clustering is chosen because it can keep the total number of clusters and the number of nodes within each cluster at a constant value. Once the data points have been aggregated using the algorithms, the silhouette score is used to choose the approach that is most suited for vehicle scheduling. The relevance of cluster centers determines the final scheduling of the vehicles. © 2024 IEEE.

关键词： Efficiency

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Face Recognition Using openCV- use case on student attendance system

Face Recognition Using openCV- use case on student attendanc...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anjan Kumar Kamalapur Begari Srikanth Asari Shivadeep Yadav Bachhanaboni Yashwanth Vivek Duraivelu Dept.of Computer Science Engineering B V Raju Institute of Technology Narsapur Telangana

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This is a modern approach to student attendance management using face recognition technology, powered by advanced techniques including Convolutional Neural Network (CNN) and Haar Cascade frontalface default algorithm. By leveraging OpenCV and these cutting-edge face recognition methods, the system offers a reliable and efficient solution for tracking attendance in educational institutions. Real-time face detection and recognition, support for multiple face recognition, and smooth interaction with current attendance management systems are some of the key characteristics. Additionally, the system incorporates deep learning algorithms to enhance its accuracy and robustness in identifying students’ faces. Through practical implementation, the project demonstrates the effectiveness of using OpenCV and deep learning for automated student attendance management. By providing accurate and efficient attendance tracking, while improving operational efficiency in educational settings, this innovative solution marks a significant advancement in modernizing attendance management practices.

关键词： Deep learning Privacy Accuracy Face recognition Neural networks Real-time systems Robustness Convolutional neural networks Face detection Character recognition

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Improvisation of QoS in SDN-Frame Work for UAV Networks Using Dijkstra Shortest Path Routing Algorithm 1

Improvisation of QoS in SDN-Frame Work for UAV Networks Usin...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mathapati, Mahantesh Nandihal, Priya Mishra, Priti Kotagi, Veeranna Dept.of Cse Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Information Science and Engineering Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India

ISBN: (纸本)9798350316469

This work presents a unique SDN-based Dijkstra shortest path routing algorithm for QoS improvisation in UAV Networks. It discusses the various problems pertaining to the networking of unmanned aerial vehicles in disaster rescue operations and unique networking algorithmic solutions to the problems. Involving the SDN technology in networks provides faster service delivery than the traditional technology, as it isolates the control plane and data plane. This SDN technology has the centralised governing management service on the distributed network that affords improved security. But, SDN with Wireless Sensor Network (WSN) does not provide enhanced Quality of Services (QoS) in sophisticated cloud computing (CC) applications. Across the research community, end-users seek to attain a QoS while accessing the cloud-based services depending on their knowledge in using today's technology. Therefore, this research study is aimed to prioritize the cloud application data without any delay from the normal data and also to provide the fastest communication using the SDN framework applying shortest path routing. The traditional networking infrastructure's speed, scalability, and agility are increased by the key functionality of SDN, which is used to intellectual the network information from the applications. Moreover, massive size data are managed and customized by using OpenContrail, which is a more responsive and scalable network. The QoS provided by the SDN with cloud-based services using shortest path, stimulates this research to apply the integrated technology in UAV. UAV can transfer the data through cloud service using SDN, in order to maintain a communication with remote experts. The proposed Shortest Path (SP) direction-finding algorithm provides appropriate deployment & connectivity to the modules of group of UAVs, also decreases time delay within the network. The simulation results reveal that the proposed technique is suitable and more effective to set up an emergency

关键词： Cloud Computing OpenStack Quality of Service Shortest Path Algorithm Software-Defined Networking UAV

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An Intelligent Security Systems in Cloud Computing using Cryptography Techniques 5

An Intelligent Security Systems in Cloud Computing using Cry...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. Intelligent Security Systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 IEEE.

关键词： Cloud security

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A System to Track Fabricated Content in Social media using Machine Learning Approach

A System to Track Fabricated Content in Social media using M...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Kavyashree, M.K. Pemula, Rambabu Harshitha, M. Vidyavardhaka College of Engineering Affiliated to Visvesvaraya Technological University Dept. of Computer Science and Engineering Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Dept. of Computer Science and Engineering Krishnarajapet Mandya India Sjce Jssstu Mysore India Vidya Jyothi Institute of Technology Dept. of Artificial Intelligence Hyderabad India Vidya Vikas Institute of Engineering and Technology Dept.of Information Science and Engg. Mysore India

ISBN: (纸本)9798350388602

In today's society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. Data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this. © 2024 IEEE.

关键词： Adversarial machine learning

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Care Compass: Monitoring made easy, Care made effective

Care Compass: Monitoring made easy, Care made effective

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Prabal Manhas Joydeep Chandra Ramanjot Kaur Rashi Sahay Computer Science & Engineering Chandigarh University Punjab India Dept.of Computer Science and Engineering Chandigarh University Mohali Punjab Computer Science & Engineering Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today’s youth. The 21 st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals.

关键词： Heart rate Cloud computing Printed circuits Real-time systems Safety Mobile applications Internet of Things Compass Wearable devices Monitoring

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Deep Learning Training Strategies for Severely Imbalanced Data in Organ Segmentation Tasks 6

Deep Learning Training Strategies for Severely Imbalanced Da...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Wang, Hsin-Hui Liu, Chin-Yun Hung, Shih-Kai Chen, Liang-Cheng Hsieh, Hui-Ling Liu, Wei-Min National Chung Cheng University Dept.of Computer Science and Information Engineering Chiayi Taiwan Tzu Chi University School of Medicine Hualien Taiwan Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation R.O.D.T.C.H Dept. of Radiation Oncology Chiayi Taiwan National Cheng Kung University Dept.of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350301953

Radiotherapy is one of the common methods for cancer treatment. Developing a radiotherapy plan requires professional medical physicists or physicians to manually contour the organ boundaries in CT series, which is time-and labor-consuming. If artificial intelligence (AI) could assist with the task, it could alleviate the workload of medical staff, especially when medical resources are tight. We propose an AI-based automatic organ segmentation system trained by clinical datasets. However, this task is prone to be non-robust models in CT image series where the background occupies the majority of the scene. To remedy such data imbalance situation, we propose adopting three strategies during the model training steps: region classification, knowledge discovery in database, and sampler. The major segmentation task is based on U-Net and ResNet34 model where all convolution layers and batch normalization are replaced with group normalization and weight standardization to ensure effectiveness in small-batch data training. In this study, 33 organs throughout the body were segmented. The ablation experiments were conducted to prove all the training models have better performance than the original method. In the future, if a hospital needs to train model with their own private datasets, the three above strategies can be adopted to prevent unsuccessful training. © 2023 IEEE.

关键词： Radiotherapy

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Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

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Multimedia Tools and Applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Padmageetha B. G Pramod Kumar Naik Mallanagouda Patil Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bangalore India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA's interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications.

关键词： Wireless sensor networks NOMA Scalability Simulation Clustering algorithms Throughput Routing Energy efficiency Routing protocols Resource management

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Secure and Proficient Provable Data Procurity With Privacy Protection in Cloud Storage

Secure and Proficient Provable Data Procurity With Privacy P...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Dr.C.Siva Kumar Lebaku Vasantha Pacha Siroshini Pullaiahgari Jagruthi Nellore Pavithra Thumati Supriya Dept.of computer science and systems engineering Sree vidyanikethan engineering college Tirupati

Cloud technology is the distribution of physical servers in such processors, memory, networks, connectivity, applications, statistics, as well as smartness only via Online platform so,in addition to allow greater innovative technology, better versatile facilities, plus scalable *** this mechanism, the data owner’s maintenance and storage costs can be reduced. Simultaneously, when the data owner loses procurity and physical authority over the data, a security threat arises. As a result, data inspection is required to ensure data integrity over a *** concern is now difficult for information owners because data authority must have been validated thereby preserving confidentiality. In the process of labelling these issues, this paradigm provides secure data acquisition as well as efficient privacy-preserving ***, it now supports multipleownership, data complexities, also an unit identification. A majority intriguing aspect for the process in which examiner will validate ownership of data having little maintenance cost. The system demonstrates that a malware platform will present evidence that is adequate to carry third-party auditors authentication of the user. The scheme’s security is analysed and demonstrated at this system to protect data privacy from third-party public auditing while making it impossible for cloud service providers avoids saving the necessary blocks to ignore the *** proposed scheme’s best intriguing feature is that it supports all of its crucial elements, such as blocking-free validation, security, process monitoring, as well as information behaviour.

关键词： Process monitoring Cloud computing Data privacy Costs Program processors Authentication Maintenance engineering

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