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2024 International Conference on Advances in data engineering and Intelligent Computing Systems, ADICS 2024

作者： Mathin, Shaik Chandra, Donthula Sharath Sunkireddy, Anudeep Reddy Varma, B.J.V. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College Of Engineering Department Of Computer Science And Engineering Hyderabad India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350364828

With the help of artificial intelligence in personalized mental health, it enhances the quality and accessibility of mental health support. Mental health issues continue to be a global concern, and advancements in AI technology offer new avenues for providing tailored support to individuals seeking assistance with their mental well-being. Its purpose is to collect information from users regarding common ailments such as anxiety, depression, and stress-related problems in plain language, which may then be retrieved using natural language processing (NLP). As a result, it is critical to determine whether the individual is suffering from mental health issues or not. Once the person's mental health problem has been validated, the type of mental health disorder can be determined. Based on the information extracted, identify the disease, and offer therapy strategies. Users of this program can interact with specialists to seek guidance, which helps to alleviate anxiety, stress, and other disorders by reducing their health issues and worries. It takes a user-centric approach to mental health treatment, integrating the power of AI with personalized care. © 2024 IEEE.

关键词： Decision trees

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification 9

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Hussain, Mohammad Manzoor Shanmugam, P. Moorthi, K. Sakthivelu, U. Rajasekar, A. Kumar, R. Naresh B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Rajalakshmi Engineering College Department Computer Science and Engineering Chennai India Sairam Engineering College Department Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamilnadu Kattankulathur India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350384208

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. The condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. The automated detection of DR necessitates categorizing colour retinal fundus pictures. This article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. The studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. The comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques. © 2023 IEEE.

关键词： Deep learning

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An Analysis of Face based eCrime Identification using First Information Report 3

An Analysis of Face based eCrime Identification using First ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Vanarasan, S. Dharani, S. Dharshini, R. Harani, S. Selvarathi, C. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Sri Sairam College of Engineering Department of Artificial Intelligence and Machine Learning Banglore562 106 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639 113 India

ISBN: (纸本)9798331509675

Face based e-crime identification initiative combines cutting-edge technologies to detect criminals in video footage, allowing for quick and effective online FIR (First Information Report) reporting. Furthermore, it uses blockchain technology to securely store and manage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crimes (eCrimes) in today's digital age poses a huge challenge to law enforcement organizations worldwide. In the face of continuously shifting criminal strategies, traditional means of identifying and reporting offenders frequently fall short. The fundamental functionality of the technology is around identifying offenders using video footage. It makes use of the potent Inception V3 algorithm, a cutting-edge deep learning model noted for its efficient image recognition skills. This algorithm analyses video frames, detects and matches facial traits, and identifies suspects. This not only improves the accuracy of criminal identification but also speeds up the investigation. In addition to identifying criminals, the streamlines the FIR reporting process. Victims and witnesses can now conveniently file FIRs online, eliminating the need for physical visits to police stations. The integration of digital reporting platforms enhances the efficiency of law enforcement agencies, making the process more accessible to the public. Blockchain technology is used to provide data security and immutability. A blockchain-based database stores all data relevant to criminal identification and FIR reporting. The combination of improved facial recognition, online reporting, and secure blockchain data management promises to make identifying and reporting criminals more efficient and secure. © 2025 IEEE.

关键词： Deep learning

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An Ensembled Real-Time Hand-Gesture Recognition using CNN 15

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, M.N. Saranya, S.S. Prasad, M. Kaviyarasu, S. Ragunath, N. Rahul, P. Kongu Engineering College Department of Computer Science and Design Tamilnadu Erode India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu Coimbatore India Controller of Examinations Tamilnadu Namakkal India Kongu Engineering College Bachelor of Science - Information Systems Tamilnadu Erode India

ISBN: (纸本)9798350370249

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems. © 2024 IEEE.

关键词： Intelligent systems

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M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

M3Hop-CoT: Misogynous Meme Identification with Multimodal Mu...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kumari, Gitanjali Jain, Kirtan Ekbal, Asif Department of Computer Science and Engineering India Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3HopCoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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Optimization of Rainfall Prediction Using Satellite data Through Machine Learning and Deep Learning Algorithms

Optimization of Rainfall Prediction Using Satellite Data Thr...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Ramani, Kasarapu Reddy, Maddireddy Srilekha Bhavani, Kelavathi Feeza, Syed Bavesh, Vellampalli Sai Dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College Department of Csse Tirupati India

ISBN: (纸本)9798350382693

In recent years, non-periodic rainfall has increased globally. The importance of accurate rainfall prediction model is increased in order to reduce the losses due to floods and drought. Rainfall prediction plays a crucial role in decision-making, flood and drought risk assessment and management. In agriculture, it guides many farmers in optimizing crop planting schedules and irrigation. It ensures better yields and reduces losses from droughts and floods. Accurate rainfall prediction benefits us in effective water resource management and energy sector planning. In India, the rainfall varies rapidly due to the vast geography. There are many factors which effects rainfall like distance from the sea, seasons, the presence of mountains, prevailing winds and precipitation. This research work mainly focuses on developing an efficient ensemble-based Machine Learning Techniques such as Extreme Gradient boosting algorithm (XG Boost) and Logistic Regression (LR) for predicting rainfall in different regions of India using satellite data and comparing results with the deep learning algorithms such as Convolutional Neural Network (CNN), Multiple Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Radial Basis Function Network (RBF). The aim of this paper is to overcome the drawbacks of existing systems and build improved prediction model with accuracy more than 90% in order to enhance water resource management, early warning systems, improved urban planning, agriculture, and climate change studies. © 2024 IEEE.

关键词： Radial basis function networks

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Secure and Transparent Client Fund Collection Protocol Enabled by Blockchain 4

Secure and Transparent Client Fund Collection Protocol Enabl...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Beevi, L Sherin Joe Prathap, P.M. Dani, W Vinil Abinaya Lakshmi, B.G. Chowdary, GurramRenuka Devi, Hema R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India R.M.D. Engineering College Dept. of Computer Science and Engineering Kavaraipettai India

ISBN: (纸本)9798331540364

Blockchain technology has evolved as a formidable instrument in the financial technology space, providing unrivalled security, transparency, and efficiency. This article presents a groundbreaking approach for safe and transparent client fund collection using blockchain technology. Addressing the flaws of traditional money collecting systems, such as security risks, opacity, and inefficiency, the protocol uses blockchain's inherent properties to revolutionize fund collection. The protocol, initiated by the client, permits fund collection requests through a secure client interface, with the blockchain network authenticating transactions using cryptographic techniques to ensure only authorized transactions are performed and immutably recorded on the blockchain.. Notably, the protocol's openness and security, which are supported by strong encryption algorithms and cryptographic protocols, reduce the danger of fraud and embezzlement while establishing trust in stakeholders. This protocol, which leverages blockchain capabilities, provides a disruptive solution for money collection, increasing responsibility and confidence in the financial industry. © 2024 IEEE.

关键词： Authentication Protocol

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Blockchain based Secure Erlang Server for Request based Group Communication over XMPP

Blockchain based Secure Erlang Server for Request based Grou...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Johnpaul, C.I. Vivekanandan, Manojkumar Premkamal, Praveen Kumar Ramya, R. St. Thomas College Department of Computer Science and Applications Kerala Thrissur India Srm University-AP School of Engineering and Applied Sciences Department of Computer Science and Engineering Andhra Pradesh Amaravati India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350333817

Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface. © 2023 IEEE.

关键词： Python

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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The Impact of the IoT on Military Operations: A Study of Challenges, Applications, and Future Prospects 4

The Impact of the IoT on Military Operations: A Study of Cha...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350307757

Military operations are evolving rapidly due to technology. With its many innovations, IoT has transformed military operations. This research explores how IoT technology influences military combat efficiency, effectiveness, and security. The military must convert from traditional to networked and data-driven operations to use IoT. This trend impacts military strategy, decision-making, and personnel welfare. The IoT can track assets in real time, monitor troop health, ensure safe supply chain management, forecast equipment repair requirements, and convert UAVs and drones. Durable communication networks boost connection and data sharing for military tasks. However, military IoT integration is difficult. Military networks are sensitive and vulnerable to cyberattacks, making security and data privacy essential. Complex concerns include interoperability, scalability, power management, environmental resilience, network congestion, and legal and ethical restrictions. Cutting-edge technology, strict cybersecurity requirements, and strategic planning are needed to optimize IoT advantages including security and efficiency. This study seeks to discuss military growth through technological change. This study emphasizes security, privacy, and operational effectiveness while utilizing IoT in military contexts. © 2024 IEEE.

关键词： Internet of things

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