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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Yamini, J. Kanimozhi, T. Jenefa, A. Bagyalakshmi, K. Aarthi, K. Dhivya, P. Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Machine learning Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Ppg Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Mathematics Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of 95.65% with a loss of 0.18 without overfitting and underfitting. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. This paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). The employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. The research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. The model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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The EcoSync: A Comprehensive Study on Water Quality Prediction and Waste Image Recognition

The EcoSync: A Comprehensive Study on Water Quality Predicti...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Ahana Mukherjee Bipasha Chatterjee Indrakshi Das Koushiki Chakraborty Madhushree Pramanik Anjan Kumar Payra Computer Science (Artificial Intelligence & Machine Learning) Institute of Engineering and Management Newtown Kolkata India Computer Science (Artificial Intelligence & Machine Learning) Institute of Engineering and Management Newtown Institute of Engineering and Management Newtown Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research was devised to analyze how machine learning tools could assess water quality and classify waste aiming to build a better system for sustaining our environment. We unified decision trees, random forests, and convolutional neural networks to create a multi-model system that identifies and sorts waste via image recognition. To strengthen our model's performance, we expanded our dataset to include sundry labelled images. Using a multilayer perceptron with Adam optimizer, our system achieved 89% accuracy in classifying waste types and 94% accuracy in identifying waste materials. Water potability analysis, however, relied on machine learning models that were trained on a comprehensive dataset of chemical and physical water parameters. The system was able to show an accuracy of 87% in predicting water safety with a low false positive rate of 3.2% when real time monitoring was integrated. These systems when integrated gave a solution that was better than traditional methods by 47% and the processing time was reduced by 62%. Our results showed that machine learning can effectively enhance environmental management systems. This scalable, technology-driven approach proved successful for both waste and water management, making it particularly valuable for regions struggling with these challenges, regardless of their development status.

关键词： Waste management Waste materials Accuracy Image recognition Biological system modeling Water quality Real-time systems Decision trees Convolutional neural networks Random forests

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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Optimizing Travel Itineraries: A Multi-Objective machine learning Model Approach 3

Optimizing Travel Itineraries: A Multi-Objective Machine Lea...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Roy, X Samuel Issac, Titus Terrance, J Sebastian Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly itinerary generator by integrating machine learning techniques. This research work explores two machine-learning algorithms: artificial Neural Networks (ANNs) and Multi-Layer Perceptron (MLP). These algorithms are evaluated for their suitability in generating user-friendly and environment-friendly itineraries. Through experimentation, the accuracy and precision of the proposed models are assessed, leading to the identification of an optimal travel planner model. The ultimate goal of this model is to provide a detailed itinerary that considers prominent modes of transportation while addressing the pressing environmental crisis. By prioritizing eco-friendly options, this approach sets itself apart from conventional travel planning tools. © 2024 IEEE.

关键词： Multilayer neural networks

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IoT Empowered AI: Transforming Object Recognition and NLP Summarization with Generative AI 2

IoT Empowered AI: Transforming Object Recognition and NLP Su...

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2nd IEEE International Conference on computer Vision and machine intelligence, CVMI 2023

作者： Joseph, Samuel Priya, Bhagavathi S. Poorvaja, R. Santhosh Kumaran, M. Shivaraj, S. Jeyanth, V. Shivesh, R.P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305142

In anticipation of the widespread adoption of augmented reality in the future, this paper introduces an advanced mobile application that seamlessly integrates AR and IoT technologies. The application aims to make these cutting-edge technologies more affordable and accessible to users while highlighting their immense benefits in assisting with household appliance control, as well as providing interactive and educational experiences. The app employs advanced algorithms such as object detection, Natural Language Processing (NLP), and Optical Character Recognition (OCR) to scan the smartphone's camera feed. Upon identification, AR controls for appliances, their power consumption, and electric bill tracking are displayed. Additionally, the application makes use of APIs to access the internet, retrieving relevant 3D generative models, 360-degree videos, 2D images, and textual information based on user interactions with detected objects. Users can effortlessly explore and interact with the 3D generative models using intuitive hand gestures, providing an immersive experience without the need for additional hardware or dedicated VR headsets. Beyond home automation, the app offers valuable educational benefits, serving as a unique learning tool for students to gain hands-on experience. Medical practitioners can quickly reference organ anatomy and utilize its feature-rich functionalities. Its cost-effectiveness, requiring only installation, ensures accessibility to a wide audience. The app's functionality is both intuitive and efficient, detecting objects in the camera feed and prompting user interactions. Users can select objects through simple hand gestures, choosing desired content like 3D generative models, 2D images, textual information, 360-degree videos, or shopping-related details. The app then retrieves and overlays the requested information onto the real-world view in AR. In conclusion, this groundbreaking AR and IoT -powered app revolutionizes home automation and le

关键词： Optical character recognition

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Water Quality Parameters Modeling of Thamirabarani River

Water Quality Parameters Modeling of Thamirabarani River

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Sri Dhivya Krishnan, K. Prabhakaran, A. Danesh, K. Dinesh Kumar, E. Saai Srivathsan, R. Sachin M, P. Sri Sairam Engineering College Department of Computer Science and Engineering Artificial Intelligence and Machine Learning Chennai India Kumaraguru college of technology Department of Automobile Engineering Coimbatore India SRM Institute of science and technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

An extensive water quality analysis of the water quality of the Thamirabarani River located in the Tirunelveli and Thoothukudi districts of Tamil Nadu, India, is conducted. This investigation focuses on 28 exclusive water parameters divided into physical, chemical, and biological parameters, including ammonia, biological oxygen demand, pH, and significant indicators. The dataset utilized for this analysis is sourced from the Tamil Nadu Pollution Control Board. This study employs machine learning algorithms inclusive of prediction and classification algorithms. Multiple-Linear Regression, Polynomial Regression, Elastic Net regression, Support Vector machine, k-Nearest Neighbors and Random Forest are used to classify the 5 Designated Best Use classes. These algorithms iterate through various combinations of considered water parameters until convergence for providing accurate insights into their interrelationships. The random forest produces 77% accuracy which is high compared to other algorithms. The outcomes of this research hold significance for policymakers and environmentalists, offering valuable insights into the water quality of the Thamirabarani River. © 2024 IEEE.

关键词： Rivers

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Scheduling the Tasks in Cloud Computing using Adaptive Adjustment in Whale Optimization Algorithm 2

Scheduling the Tasks in Cloud Computing using Adaptive Adjus...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Nithyashree, G.D. Rajashekhar, S.A. Zanke, Pankaj Navyashree, K.S. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350381665

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm's iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm's exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account. © 2024 IEEE.

关键词： Cloud computing

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Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-trust Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2312-2327页

作者： Wang, Tingting Fang, Kai Cai, Jijing Wang, Lina Tian, Jinyu Feng, Hailin Li, Jianqing Guizani, Mohsen Wang, Wei Macau University of Science and Technology Faculty of Innovation Engineering School of Computer Science and Engineering Macau999078 China Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Machine Learning Department United Arab Emirates Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%. © 1983-2012 IEEE.

关键词： Economic and social effects

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IoT-Deep learning Based Activity Recommendation System

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computer Systems science & engineering 2023年第5期45卷 2001-2016页

作者： Sharmilee Kannan R.U.Anitha M.Divayapushpalakshmi K.S.Kalaivani Department of Electronics and Communication Engineering Nandha Engineering CollegePerundurai638052TamilnaduIndia Department of Computer Science Sona College of Arts and ScienceSalem636005TamilnaduIndia Department of Artificial Intelligence and Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabad500090TelanganaIndia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060TamilnaduIndia

The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global *** professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical *** Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health *** Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have *** existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient ***’health can be continuously monitored using advanced WS that can be fitted or embedded in their *** research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and machine learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep learning(DL)+Restricted Boltzmann machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it *** recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard *** a healthcare DSS,this framework is clever enough to detect and analyze a patient’s *** results further reveal that the proposed system is efficient and clever enough to deliver health *** data reported in this study demonstrate the *** device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.

关键词： Deep learning IoT healthcare system activity recommender system body sensors

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