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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

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Deep Learning Model for Human Detection-Enhanced YOLO Comparative Analysis 4

Deep Learning Model for Human Detection-Enhanced YOLO Compar...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, P. Aswathy, R.H. Soms, Nisha Niranjani, V. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri eshwar college of engineering Department of Computer science and engineering Coimbatore India

ISBN: (纸本)9798331540364

Video surveillance systems employing closed-circuit television (CCTV) are crucial for public safety. Accurately detecting humans within video frames is a core component of this technology. This knowledge is crucial for numerous uses, including detecting possible dangers, observing crowd dynamics, and examining patterns of human behavior. This research introduces a new method called YOLOv8, designed to enhance human detection capabilities within CCTV footage. The proposed algorithm, YOLOv8, is built upon the popular object recognition framework, YOLO, but with significant enhancements to improve its accuracy and efficiency in detecting humans. YOLOv8 utilizes deep convolutional neural networks (CNNs) and advanced object recognition techniques to achieve high accuracy in human detection, even in challenging environments such as low light conditions, occlusions, and varying scales of human poses. To assess the effectiveness of YOLOv8, various tests were performed using standard datasets and real-world CCTV surveillance footage. The results demonstrate that YOLOv8 outperforms existing human detection algorithms regarding accuracy, robustness, and real-time processing capability. YOLOv8 achieved an average precision of over 95% in human detection, making it highly suitable for everyday CCTV surveillance. The proposed YOLOv8 algorithm has the potential to significantly improve human detection in security camera footage and enhance public safety. This research contributes to advancing human detection technology and can benefit various domains, including law enforcement, public safety, and video analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Analysis of Liver Disease Using Random Forest Machine Learning Algorithm 1

Predictive Analysis of Liver Disease Using Random Forest Mac...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vetrithangam, D. Kumar, Nallabariki Praveen Arya, Neha Tiwari, Amrita Raju, G. Jaya Srinivas, Gollapalli Veera Satya Chandigarh University Department of Computer Science & Engineering Mohali India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Aditya College of Engineering and Technology Department of Mechanical Engineering Surampalem India

ISBN: (纸本)9798350376517

The proposed system's objective is to improve the performance of diagnosing liver diseases through machine learning by using the Random Forest algorithm. Such systems accommodate a detailed database that comprises of several parameters of the patient's health status, through which important factors that cause liver disease may be determined. The analysis utilizes Random Forest, a feature selection technique that boosts the efficacy of the model's results in determining the outcomes of liver diseases with accuracy and recall. The training is conducted to reduce the component's error rate as well as to determine the ability of the system to perform complex tasks. Likewise, the proposed solution also focuses on the easy-to-use front-end for the target groups - the healthcare organizations - so the solution perfectly fits into their day-to-day work. In sum, this system is designed to become an innovative, cutting edge, and evidence-based tool for the notification and prevention of the liver disease in its early stage so as to enhance patients' outcomes and make more efficient use of the health care facilities. © 2024 IEEE.

关键词： Contrastive Learning

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An Improved Fuzzy Grounded Dynamic Package Priority Resolve and Line Management System in Wireless Sensor Network

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SN computer science 2025年第2期6卷 1-12页

作者： Songare, Lokendra Singh Kumhar, Bhupchand Rajput, Ajeet Singh Shukla, Prashant Kumar Nigam, Ankita Gautam, Prakhar Ahluwalia, Ritu Department of Computer Science and Engineering Medicaps University MP Indore India Department of Computer Science & Engineering IES Institute of Technology and Management IES University MP Bhopal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L Deamed to be University Vaddeswaram Andhra Pradesh Guntur 522302 India Princeton Institute of Engineering and Technology for Women Telangana Hyderabad India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSBS Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

In Wireless Sensor Networks, it is crucial to schedule packets efficiently while taking priorities into account. It helps congestion avoidance algorithms decide on rate adjustments and packet discarding and mitigates the performance hit from WSN’s resource constraints. The current PPI method works well for use cases where the threshold values are already known. FDMP (Fuzzy Based Dynamic Packet Priority Determination and Line Management) is presented in this article. FDMP is a congestion-aware packet scheduling algorithm that offers robust and universal mechanisms for dynamically determining packet priority and efficient line management. Therefore, it achieves minimum end-to-end delay for high priority packets while providing adequate fairness for low priority packets by scheduling packets within each line according to their execution time. From our simulations, we can deduce that the FDMP decreases end-to-end transmission delay and jitter while increasing packet delivery ratio, average residual energy, and throughput. Congestion in the network can be avoided through the optimal scheduling of packets to prevent packet retransmission and the removal of packets with expired deadlines from the transmission medium. When compared to the Dynamic Multilevel Priority (DMP) line scheduling approach, which is also a resource control algorithm, FDMP demonstrated superior performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DMP FDMP PPI QOS WSN

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TSInc: Tie strength based incremental community detection using information cascades

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International Journal of Information technology (Singapore) 2024年第5期16卷 3243-3253页

作者： Das, Soumita Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Assam Silchar 788010 India

Community detection in dynamic networks is of great importance in social network analysis. For instance, online social networks such as Facebook, WhatsApp and LinkedIn are rapidly evolving with time. However, the majority of previous research have focused on community detection in static networks. It is computationally expensive to repeatedly use a static algorithm on snapshots of dynamic networks. In this paper, we propose a community detection algorithm namely, Tie Strength based Incremental Community detection (TSInc), aiming to find communities and track the dynamic events in each snapshot. TSInc determines communities using tie strength based information cascades. Following the initial snapshot, we construct a new subgraph based on the dynamic events in the subsequent snapshots and quickly assign communities. These communities in combination with the unchanged communities are merged to obtain the inherent community structure. These procedure is repeated for the remaining snapshots as well. Experimental results on several real-world dynamic networks and state-of-the-art approaches shows that TSInc algorithm outperforms the baseline algorithms in terms of effectiveness of the detected communities. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cascades Community Dynamic networks Information Diffusion Tie Strength Tie strength

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Deep Learning Based Cervical Cancer Detection Using Hunter Prey Optimizer Algorithm

Deep Learning Based Cervical Cancer Detection Using Hunter P...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Arikaran, N. Shanmugapriya, S. Shamili Sathishkumar, R. Barseth, A. Mohamed Sai, S. Sriram Vishvesh, S. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

Deep learning (DL) is a widely employed technique in many different sectors these days, including medicine. Many problems that may be difficult to solve with traditional synthetic intelligence (AI) strategies can be solved with DL strategies. With more than seven hundred deaths each day, cervical cancer (CC) is the second leading cause of death in women after cancer. By 2030, this number is projected to reach 400,000 each year. Nonetheless, cancer is entirely treatable if it is discovered in its early and precancerous phases. The two most well-known screening strategies for identifying cervical cancer are the Pap smear and colposcopy. but the manual screening technique has a high error rate due to human error. In order to get around this obstacle, machine learning. Nowadays, deep studying (DL) is a method this is broadly used in many other fields, consisting of medication. DL techniques may handle a wide range of problems that are difficult to solve with traditional artificial intelligence (AI) methods. Cervical cancer (CC) is the second most common cause of death for women, after breast cancer, accounting for about 700 deaths every day. This number is expected to increase to 400,000 annually by 2030. However, if cancer is detected in its early and precancerous stages, it is completely curable. Pap tests and colposcopies are the two most widely used screening methods for the detection of cervical cancer. However, due to human error, the manual screening process has a high false rate win over this project, machine learning. The proposed model achieved 97.8% ACC, 95.4% Spec, 95.7% Recall, 98.2% Prec, and 97.6% F1-Score. © 2024 IEEE.

关键词： Diseases

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Multi-layered access control based auto tuning relational key implications in enterprise-level multi-tenancy

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Multimedia Tools and Applications 2025年第17期84卷 17805-17836页

作者： Henge, Santosh Kumar Rajakumar, R. Prasanna, P. Parivazhagan, A. Hu, Yu-Chen Chen, Wu-Lin Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation KL University AP Vaddeswaram India Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Chennai India Department of CSE Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Science Xitun District Tunghai University No. 1727 Sec. 4 Boulevard Taichung City407224 Taiwan Department of Computer Science and Information Management Providence University Taichung City Taiwan

This article discusses the importance of cloud-based multi-tenancy in private–public-private secure cloud environments, which is achieved through the isolation of end-user data and resources into tenants to ensure data confidentiality and integrity. Maintaining secure access control is a complex and critical task in multi-tenant cloud environments, and researchers have proposed innovative methods for implementing security, privacy, and authenticity. However, several challenges remain. The proposed research addresses these challenges by presenting a layered approach to multi-tenancy in an enterprise-level cloud environment. The study has three main objectives: (1) to describe and analyze the complexities of enterprise-based multi-tenancy and proposed technical solutions in a tabular format;(2) to propose multi-tenant test cases across application, software, infrastructure, and network as a service environment;and (3) to present a collaborative, relational optimal key-based multi-tenancy access control model. The proposed access control model involves integrating various secure layers, such as intranet, extranet, intra-extranet, and using feasible relational optimal keys to provide secure multi-tenant access control among various tenants of single and multiple enterprises. This model provides secure data transmission by integrating various multi-layer data filters and secure blades. The layered multi-tenancy approach offers many benefits and technical feasibilities to avoid security and privacy issues in enterprise-based multi-tenant environments. The process achieved a 93% secure mapping rate to authenticate all IN and OUT data access transactions among the various multi-tenancy of similar and distinct enterprises. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtualization

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Curriculum Guided Domain Adaptation in the Dark

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2604-2614页

作者： Jahan, Chowdhury Sadman Savakis, Andreas Rochester Institute of Technology Center for Imaging Science RochesterNY14623 United States Kate Gleason College of Engineering Rochester Institute of Technology Department of Computer Engineering RochesterNY14623 United States

Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters. The need for domain adaptation of black-box predictors becomes even more pronounced to protect intellectual property as deep learning based solutions are becoming increasingly commercialized. Current methods distill noisy predictions on the target data obtained from the source model to the target model, and/or separate clean/noisy target samples before adapting using traditional noisy label learning algorithms. However, these methods do not utilize the easy-to-hard learning nature of the clean/noisy data splits. Also, none of the existing methods are end-to-end, and require a separate fine-tuning stage and an initial warmup stage. In this work, we present Curriculum Adaptation for Black-Box (CABB) which provides a curriculum guided adaptation approach to gradually train the target model, first on target data with high confidence (clean) labels, and later on target data with noisy labels. CABB utilizes Jensen-Shannon divergence as a better criterion for clean-noisy sample separation, compared to the traditional criterion of cross-entropy loss. Our method utilizes cotraining of a dual-branch network to suppress error accumulation resulting from confirmation bias. The proposed approach is end-to-end trainable and does not require any extra fine-tuning stage, unlike existing methods. Empirical results on standard domain adaptation datasets show that CABB outperforms existing state-of-the-art black-box DA models and is comparable to white-box domain adaptation models. © 2020 IEEE.

关键词： Curricula

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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HarvestHub: Mobile Agricultural Markets 7

HarvestHub: Mobile Agricultural Markets

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Deepika, Gundaboina Begum, Heena Reddy, Gangireddy Niranjan Joshini, Gogulamanda Sathyanarayanan, D. Pallavi, L. B V Raju Institute of Technology Department of Computer Science and Engineering Telangana India B V Raju Institute of Technology Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350372816

This project aims to develop a web application addressing challenges in agricultural marketing, leveraging technology to connect farmers directly with buyers. Recognizing technology's pervasive role and potential impact, the project seeks to create a user-friendly platform for agricultural *** project will employ a comprehensive approach, considering technical development and socio-economic contexts. It begins with analysis of existing systems, informed by literature review and consultations. Iterative cycles of design, implementation, and testing guided by user feedback will be conducted. Key features include support for a local language, secure payments, customizable categorization, and intuitive interface *** project aims to produce a web application tailored for agricultural marketing. It will provide farmers a platform to showcase products and offer buyers convenient access. By streamlining processes, the application intends to enhance market access, improve transparency, and contribute to sector growth and *** by a commitment to leveraging technology for social good, this project seeks to empower farmers and promote economic development. Through stakeholder collaboration and ongoing evaluation, it aims for sustainability and scalability. © 2024 IEEE.

关键词： Agricultural economics

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