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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman, Arifur Moinuddin, Moinuddin Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350335095

Blockchain and hyperledger are decentralized approaches used for maintaining security, secrecy, and integrity of organization based business transactions. In our study we have built a permissioned blockchain using HyperLedger Fabric framework that able to handle data storage and secure intra-and inter-organizational communication. The key reason for which we have chosen HyperLedger Fabric framework is that, it uses RAFT consensus algorithm that provides permissioned blockchain network by creating a channel that was unavailable to the public blockchain network. We have made a smart contract for the permissioned blockchain by developing and writing the chaincode. We have used Fabric-CA server to generate crypto material. Then we have set up the network configuration, organizations, ordered peers, endorsing peers, anchor peers and developed client application for Hyperledger Fabric. We have integrated the web-front with fabric using WEB-API. © 2023 IEEE.

关键词： Blockchain

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Machine Versus Human Intelligence: An Illustration from the CSES Problem Set

Machine Versus Human Intelligence: An Illustration from the ...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Dutta, Anurag Ray, Samrat Kumarasankaralingam, Lakshmanan Kumar, Pijush Kanti Ghosh, Soubhik Kumar, Padmanavan Government College of Engineering & Textile Technology Department of Computer Science Serampore India International Institute of Management Sciences Maharashtra Pune India Saint Joseph University Department of Mathematics Nagaland India Government College of Engineering & Textile Technology Department of Information Technology Serampore India Bengal College of Engineering And Technology Department of Computer Science Durgapur India Kalinga Institute of Industrial Technology Department of Computer Science Odisha Bhubaneswar India

ISBN: (纸本)9798350379716

Following the release of CHATGPT in November 2022, both textual and visual LLMs evolved a long way. Several comments have been made by experts on the expertise and intelligence possessed by these Large Language Models;Amongst all, the contest between Human and Machine Intelligence is that discussion that catches great heights. This research focuses on the analytical and reasoning abilities of LLMs and further compares them to those of Human Intelligence. As a benchmark for the same, the CSES Problem Set, a well-structured set of algorithmic problems, touches a wide spectrum of computational syllabi, like Data Structures, Mathematics, Decision Theory, etc. Our analysis of the contrast in abilities leads to the conclusion that LLMs need a long way to go, and thus LLM researchers rather than modeling more and more visually appealing LLMs (like Sora, Dall-e, etc.) should focus more on the abilities of the existing textual models like CHATGPT. Our research shows that the Machine Intelligence is ≈ 46 % lower than that of the Human Intelligence. © 2024 IEEE.

关键词： Problem oriented languages

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Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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Brain MRI Image Analysis for Alzheimer’s Disease (AD) Prediction Using Deep Learning Approaches

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SN computer science 2024年第1期5卷 160页

作者： Singh, Archana Kumar, Rakesh Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur 273010 India

Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative condition that causes memory impairment decline. Alzheimer’s disease (AD) stands as one of the most pervasive chronic ailments affecting the elderly population, presenting a considerably high prevalence rate. The significance of early detection in the treatment of AD cannot be overstated, owing to the potentially severe cognitive and neurological impairments that emerge as the disease progresses. Swift and accurate diagnosis holds a pivotal role in curbing the extent of brain deterioration that becomes more pronounced in the later stages of the disease’s course. Many strategies have been used to determine the pattern of disease to detect Alzheimer’s disease. The similarity of brain patterns in older people and different stages of Alzheimer’s disease makes categorization difficult. In recent times, the domain of medical imaging has witnessed a surge in the prominence and accomplishments of Deep Learning (DL) and Machine Learning (ML) techniques. These advancements have not only taken center stage in the evaluation of medical images but have also ignited substantial enthusiasm for enhancing the diagnosis of Alzheimer’s disease. Within this landscape, Deep Learning and machine models have risen to prominence, outpacing conventional Machine Learning methods in precision and efficiency, particularly in the realm of Alzheimer’s disease detection. A pivotal approach that has gained traction involves the utilization of pre-trained Convolutional Neural Network (CNN) models. These models, primed with extensive prior learning, exhibit an enhanced ability to categorize various stages of Alzheimer’s disease. Through the adept utilization of Deep Learning models, the categorization of six distinct phases of Alzheimer’s disease becomes attainable, offering a promising avenue for refining diagnostic accuracy and comprehension. Normal control (NC), Significant memory concern (SMC), Early mild cognitive impair

关键词： Alzheimer’s disease CNN pre-trained models MRI MRI pre-processing

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Automated Exoskeleton Prosthetic Suit With Monitoring System 4

Automated Exoskeleton Prosthetic Suit With Monitoring System

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Jeeva, C. Sagar, Lalit Kumar Srivastava, Mudit P. Abinaya, R. Narmatha, M. Shanmuga Priya, M. Sri Sairam Engineering College Department of Electrical and Electronic Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science Engineering Delhi India Srm Institute of Science and Technology Department of Physics Delhi India

ISBN: (纸本)9798331508845

The Automated Exoskeleton Prosthetic Suit with Monitoring System aims to develop a fully functional prosthetic suit with coordinated strength and response time. The suit is ameliorative and can be worn or removed as needed. Designed as a wearable inner suit, it operates autonomously, incorporating sensors and navigation systems to reduce inequality. In regions where the suit is worn, it analyzes blood flow using ultrasound Doppler principles and aids in improving circulation. The exoskeletons support or replace impaired body parts, allowing users to function properly. Electrical motors and precise position control form the core of its most common design. This suit explores the entire body using various actuators, tasks, and degrees of freedom. The product can be worn as an inner suit, featuring an autonomous sensor and navigation system to eliminate inequality. It measures blood flow in the regions covered by the suit, enhancing circulation. The primary goal of the product is to detect even small objects and prevent the wearer from falling. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Advanced Machine Learning Techniques for Predicting Health Status in Smoke-Exposed Environments 2

Advanced Machine Learning Techniques for Predicting Health S...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Duraisamy, Premkumar Nitheshvaran, B. Kumar, Nalin S. Irshad, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350340235

S moker health status is a vital focus in public health and medical research due to smoking's impact on preventable diseases and premature death worldwide. Recognizing the diverse effects of smoking on health is crucial, spanning both immediate and long-term consequences across multiple physiological systems. This research underscores the pressing importance of comprehending smoking's far-reaching impacts on health and emphasizes the necessity of effective interventions and cessation strategies to mitigate i ts adverse effects. This study aims to utilize diverse machine learning algorithms to predict the health status of smokers accurately. The primary objective is to equip healthcare professionals with a reliable diagnostic tool for early and precise identifi cation of smoking-related health risks, ultimately saving lives. Leveraging a comprehensive dataset, various algorithms, such as Boost, LightGBM, CatBoost, Random Forest, AdaBoost, and others, are explored. Through rigorous experimentation, the Boost algorithm stands out, achieving an impressive 96.2% accuracy in predicting the health status of smokers. © 2023 IEEE.

关键词： Classification Kaggle dataset Machine learning Smoker health status Voting ensemble method

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Decentralized Generative AI Model Deployment Using Microservices

Decentralized Generative AI Model Deployment Using Microserv...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Jhingran, Sushant Bansal, Nidhi Chaturvedi, Rekha Singh, Ajeet Arora, Yojna School of Engineering and Technology Sharda University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Pune India School of Information Security and Data Science Manipal University Jaipur Department of Data Science and Engineering Jaipur India Moradabad Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Moradabad India

ISBN: (纸本)9798331517953

The microservices are continuously growing in domains of communication using cloud and AI. Access to microservice can be done using any respective cloud environment. Access microservices using cloud require multiple configurations. These configurations can be set up in Kubernetes or containerized environments to lightweight any application which increases the performance of microservices. Multiple requests can create a concurrent call, which causes slow response. Generative AI models can be used to solve concurrent calls. Generative AI used a modular approach for microservice communications with API gateway. This allows microservices to function in a variety of contexts, including cloud, edge devices, and containers. This process refers to a decentralized approach which can only be implemented in the case of microservices and provide improvement in latency along with bandwidth. © 2024 IEEE.

关键词： Cloud Container Docker Generative AI Microservices

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Evaluating Security Risks and Protecting Conversational AI: An in-Depth Vulnerability Assessment and Security Testing of the ChatGPT Android Application 2nd

Evaluating Security Risks and Protecting Conversational AI: ...

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2nd International Conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Majeti, Srinadh Swamy Shyam Prasad, T. Thota, Sunil Kumar Department of Artificial Intelligence Anurag University Hyderabad India Department of Computer Science and Engineering Anurag University Hyderabad India Department of Computer Science and Engineering Keshav Memorial Institute of Technology Hyderabad India

ISBN: (纸本)9789819747269

Recent years have seen a surge in the popularity of conversational AI applications like ChatGPT, revolutionizing human–machine interactions. However, as these AI chat systems become more integrated into daily life, ensuring their security is paramount. This research paper conducts a thorough security assessment of ChatGPT, using static and dynamic testing. The study uncovers significant vulnerabilities, including potential data leaks and susceptibility to adversarial attacks, raising concerns about user privacy and potential exploitation. Recommendations for mitigation are provided to enhance ChatGPT's security. This research contributes to the growing body of knowledge on conversational AI security, stressing the importance of proactive measures to protect user data and maintain a secure conversational experience. It also highlights the necessity for ongoing security assessments as AI technology advances. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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Towards Enhancing Child Safety: A Deep Learning Approach to Detect Child Safe and Unsafe Objects

Towards Enhancing Child Safety: A Deep Learning Approach to ...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Khan, Fairuz Areefin Dey, Ashim Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chittagong4349 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331535476

The safety and well-being of children are a paramount concern, and the accurate detection of potentially haz-ardous objects is crucial in ensuring their protection, especially in indoor situations. In the Artificial Intelligence (AI) age, the future belongs to domestic robots;they will be used for child education, surveillance, and healthcare support. Early identification of unsafe objects and intervention by these next-generation social family robots can play an essential role in providing a safer environment for children. Keeping these issues in mind, this study aims to tackle daily risks and accidents that endanger children by using computer vision to detect unsafe objects. To achieve this goal, a unique dataset namely ChildSUn is introduced containing approximately 5350 images of child-safe and unsafe objects of ten categories. Various CNN architectures, including custom CNN, VGG16, VGG19, ResNet50, Inception-V3, EfficientNet-B0, and MobileNetV2, are implemented on the prepared dataset. A deep dive analysis is performed on the implemented models. The EfficientNet-B0 model showed exceptional performance, reaching a remarkable level of accuracy of 99.63% on the test dataset. These findings underscore the potential of EfficientNet-B0 as an effective solution for real-world child safety applications and encourage further exploration of deep learning techniques to enhance child protection globally. We hope this work will pave the way for future social household robot development, which will ensure child safety in any place. © 2024 IEEE.

关键词： Deep learning

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Smart Agricultural Greenhouse System: A Context-Aware Application

Smart Agricultural Greenhouse System: A Context-Aware Applic...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Swain, Sujata Niyogi, Rajdeep School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science IIT Roorkee Roorkee India

ISBN: (纸本)9783031513374

Smart applications are getting more powerful and cheaper cost due to the advancement in sensor technology. In this chapter, we have considered a smart greenhouse application. The important parameters of the smart greenhouse are to control light, temperature, humidity, and water. Greenhouse can provide a favourable growth environment for plants. This smart greenhouse system is a context-aware application. When the context changes in the environment, the application automatically adapts it and does the changes seamlessly without user intervention. In this chapter, we propose a model for the smart greenhouse system. The proposed model can adapt to context changes such as temperature rises, and it automatically turns on the AC seamlessly. The proposed model is based on the machine learning approach. The model maintains the greenhouse temperature, humidity, and sunlight duration in the suitable ranges of the plant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context Greenhouse Machine learning NLP Sensors Web service

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