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检索条件"任意字段=3rd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2020"

共 814 条记录，以下是21-30 订阅

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Malicious URL Detection Using artificial intelligence Techniques 3rd

Malicious URL Detection Using Artificial Intelligence Techni...

引用

3rd international conference on computing, Communication and Learning, CoCoLe 2024

作者： Suresh, Yeresime Gowda, Rudra Abhijeth, Virat Sanjana, K. Shodha, H.M. Department of CSE - Artificial Intelligence Ballari Institute of Technology and Management Ballari India Department of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (纸本)9783031790409

Web has performed a vital part in the advancement of modern technology, making it easy for the people to access information from anywhere in the world. A huge amount of data is available on the web that are both secured and unsecured. Secured data can only be accessed by individuals with proper authentication credentials, while unsecured data can be accessed easily. The data and services are accessed on the web via URL (Uniform Resource Locator). With the increasing frequency of secured data on the web, there has been an equivalent rise in the number of cyber attacks that are aimed in gaining the unauthorized access to the secured data via malicious URLs. But most of these attacks can be reduced if the URL is legitimate. By extracting and evaluating the different parts of the URL, it is easy to conclude whether the URL is malicious or legitimate. So, in order to identify and classify these URLs to be malicious or legitimate, various machine learning algorithms are available and one can employ deep learning strategies to implement this problem statement. The Highest obtained training accuracy is 99.02% and highest testing accuracy obtained is 97.06%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Phishing

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Network Coding For Data Compression In Genomic Sequencing

Network Coding For Data Compression In Genomic Sequencing

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Disruptive Technologies (ICDT), international conference on

作者： Sultan Alam Saksham Agarwal Anshumaan Sharma School of Computing Science and Engineering VIT Bhopal University Sehore M.P. India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore M.P. India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The exponential growth of next-generation sequencing technologies has resulted in a massive influx of genomic data, posing significant challenges in storage, transmission, and processing. Conventional compression methods, such as gzip and bzip2, are not optimized for the unique structural patterns and redundancies of genomic sequences, leading to suboptimal performance. Addressing this gap, our research introduces a novel data compression framework that leverages network coding principles to enhance efficiency, scalability, and reliability. Network coding, widely used in communication systems, is adapted here to process and compress genomic data by employing finite field arithmetic and redundancy-aware encoding techniques. Our method achieves a compression ratio of up to 50% while maintaining a data integrity level of 99.99%, as validated through extensive mathematical analysis and experimental testing on diverse genomic datasets. Furthermore, the framework demonstrates linear scalability and robust error resilience, making it particularly suited for large-scale applications in clinical genomics and research. By bridging the gap between traditional compression algorithms and the growing demands of modern genomic data, this study lays the groundwork for more efficient and reliable data management solutions in bioinformatics.

关键词： Sequential analysis Scalability Data integrity Genomics Data compression Network coding Reliability Bioinformatics Resilience Testing

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Dynamic Resource Allocation in Edge computing via Deep Deterministic Policy Gradient Reinforcement Learning

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Vidhya Gopal U Surendar A Bajulunisha M Tamilselvi V Sathiyapriya N Saranya Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering SRM institute of Science and Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Department of Computer Science and Engineering Roever Engineering College Perambalur India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST–WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment.

关键词： Computational modeling Simulation Markov decision processes Neural networks Reinforcement learning Quality of service Dynamic scheduling Resource management Time factors Edge computing

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Breaking Language Barriers for Real-Time Translation Solutions in Tourism Using Cloud computing and AI

Breaking Language Barriers for Real-Time Translation Solutio...

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international Symposium on Advanced computing and Communication (ISACC)

作者： S. Lourdu Jame C. Shobana Nageswari S. Jayaprakash E. Punarselvam R. Meenakshi M. Muthulekshmi Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. Cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions.

关键词： Cloud computing Adaptation models Translation Accuracy Scalability Computational modeling Real-time systems Natural language processing User experience artificial intelligence

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Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using artificial intelligence

Data Augmentation for Cognitive Behavioral Therapy: Leveragi...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Bosubabu Sambana Kondreddygari Archana Suram Indhra Sena Reddy Shaik Meethaigar Jameer Basha Shaik Karishma Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analyze these cognitive pathways, which could be critical for psychotherapists aiming to deliver timely and effective interventions in online environments. Cognitive Behavioral Therapy (CBT) framework leveraging acceptance, commitment and data augmentation to categorize and address both textual and visual content as positive or negative. Specifically, the system employs BERT, RoBERTa for Sentiment Analysis and T5, PEGASUS for Text Summarization, mT5 for Text Translation in Multiple Languages focusing on detecting negative emotions and cognitive distortions within social media data. While existing models are primarily designed to identify negative thoughts, the proposed system goes beyond this by predicting additional negative side effects and other potential mental health disorders likes Phobias, Eating Disorders. This enhancement allows for a more comprehensive understanding and intervention strategy, offering psychotherapists a powerful tool for early detection and treatment of various psychological issues.

关键词： Sentiment analysis Social networking (online) Mood Anxiety disorders Medical treatment Mental health Data augmentation Distortion Classification algorithms artificial intelligence

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Ethics of AI in the Educational Sector - Navigating the Moral Landscape 3rd

Ethics of AI in the Educational Sector - Navigating the Mora...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Manoharan, Geetha Ashtikar, Sunitha Purushottam Dudhagara, Chetan R. School of Business SR University Telangana Warangal India International Agribusiness Management Institute Gujrat Anand India

ISBN: (纸本)9783031862984

This chapter investigates the ethical considerations surrounding the rapidly expanding digital area of artificial intelligence (AI) and the challenges of applying it to the education sector. Initially, the influence of AI on modern education is examined, along with its possible benefits (such as customized instruction and reduced administrative tasks) and drawbacks (like worries about data privacy, inclusiveness, and accessibility). We look at significant ethical concerns while delving into this fascinating area, such as how AI may affect student privacy, the potential for algorithmic bias, and the requirement that AI systems be honest. We investigate the potential downsides of predictive analytics and data-driven education, carefully examining the trade-off between personalization and privacy. We also discuss the problem of algorithmic fairness and how machine learning algorithms might reinforce existing inequalities. As 2023 draws near, one of the most significant technical trends in higher education is artificial intelligence (AI). By offering customised instruction based on each student’s needs and facilitating creative approaches to accessibility, artificial intelligence (AI) can improve the educational experience. AI has the potential to improve student interaction with course materials, which will lead to positive results. However, it is critical to recognise the potential ethical issues that need to be addressed, such as maintaining equity, reducing prejudice, and addressing the possibility that AI could eventually replace human instructors in the classroom. The need of accountability and transparency in artificial intelligence-based teaching technologies is emphasised in this chapter. Next, consideration is given to offering moral guidelines for the application of AI in education. We support the establishment of a framework that gives openness, inclusiveness, accountability, and a student-centered approach top priority. This chapter adds a great deal to the

关键词： Differential privacy

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863042

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using Machine Learning Techniques;scrutinize Search Engine Optimization Strategies with artificial intelligence to Rank a Website;a Study on Machine Learning algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on Machine Learning algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention Mining for Mental Health Diagnosis Using Logistic Regression;machine Learning for Accessible and Precise Assessment in Smart Monitoring Systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on Data Mining and Machine Learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive Learning Techniques;asthma Prediction Using Fuzzification and Machine Learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep Learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of Machine Learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024