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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Selva Birunda, S. Nagaraj, P. Jency A Jebamani, B. Revathi, B. Muneeswaran, V. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

The main purpose of making this report is to give the information to the franchise to make their decision on buying the players by bidding in the auction based on their performances. There are many models of reports which describe the IPL seasons. This report's purpose is to give a brief analysis of the IPL 202215th league. In this report, much of the data from the datasets of matches is utilized and analysis is made by using Python packages specifically, Pandas, NumPy, and matplotlib. By making graphs and tables a brief analysis with complete knowledge of an IPL season is given. This analysis describes every good performance and gives good chance to the best players. © 2023 IEEE.

关键词： data visualization

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Automatic Road Extraction using Scale Invariant Feature Transform-based Random Forest in Satellite Images 1

Automatic Road Extraction using Scale Invariant Feature Tran...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Rajesh, N. Alzubaidi, Laith H. Shivaprasad Yadav, S.G. Chembian, W.T. Prameela, Boddepalli Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India Department of Computer Science and Engineering Chennai India Vignan's Institute of Information and Technology Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350352931

Road Extraction from High-Resolution Satellite Images (HRSI) plays a significant role in the analysis of the remote sensor image field. The extraction of road automatically labels a road's pixels in a satellite image with semantic features depending on topographical meaningful features. However, manual road extraction inaccurately extracts road networks from satellite images which has significant difficulties like complex urban environments, varying road types, and diverse image conditions. Therefore, the Scale Invariant Feature Transform-based Random Forest(SIFT-RF) is proposed for automatic road extraction in satellite images. First, the image is obtained from DeepGlobe dataset and Z-score normalization is employed to maximize model performance. Then, SIFT is established to extract the road network in satellite images. Finally, the RF is utilized to extract and classify roads as road or non-road using Machine Learning (ML). The SIFT-RF achieves a greater f1-score of 98.24% compared to existing methods like Dual-Attention Road extraction Network (DA-RoadNet), High-Resolution U-Net architecture (HRU-Net), and Global Context-aware and Batch-independent Network (GCBNet) respectively. © 2024 IEEE.

关键词： Random forests

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AI-Driven Approaches to Enhancing Mental Wellbeing and Stress Relief

AI-Driven Approaches to Enhancing Mental Wellbeing and Stres...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Revathi K S. Priyanka Priyadharshini S Niranjan NN Visalachi N P. Sumathi Department of Information Technology SNS College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Information Technology & Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Mental health issues affected millions of peoples in worldwide, who often struggle with stigma, high costs, and low access to real life care. Conventional therapy methods faced lot of challenges to identify the issues. These challenges need to highlight for more accessible, affordable, and effective mental health solutions. Traditional models of care often fall short of providing adequate support to all those in need, leading to gaps in care and disparities in mental health outcomes. So, need for Innovation in Mental Health Support for effective mental health solutions to address these challenges. Introduction to “InnerPeaceAI” Aims to provide personalized, on-demand therapy using advanced AI technology, offering a confidential and user-friendly platform to improve mental health care accessibility. InnerPeaceAI aims to bridge the gap between the need for mental health care and its accessibility by leveraging advanced AI technology to deliver therapy sessions tailored to individual needs. The platform uses natural language processing and machine learning algorithms to create personalized treatment plans, identify mental health issues, and provide real-time support to users. This innovative approach allows for immediate access to therapy, without the constraints of time, location, or cost.

关键词： Technological innovation Costs Machine learning algorithms Medical treatment Mental health Depression Real-time systems Natural language processing Time factors artificial intelligence

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White Blood Cells Classification Model using Automatic Soft Computing

White Blood Cells Classification Model using Automatic Soft ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mahale, Pragati Prashant Deshmukh, Rakesh K. Pise, Priya Rangari, Sudhir Ramrao Kalinga University Raipur Department of Information Technology AISSMS's Institute of Information Technology Pune India Kalinga University Raipur India Indira College of Engineering and Management Artificial Intelligence and Data Science Pune India JSPM'S Jayawantrao Sawant College of Engineering Information Technology Department Pune411028 India

ISBN: (纸本)9798350361537

Accurate classification of blood cells, especially WBCs, is crucial for disease diagnosis and management. Traditional optical microscopic imaging provides limited information, but hyperspectral imaging (combining spatial and spectral data) offers richer insights. There are challenges and opportunities in white blood cell (WBC) classification using both Convolutional Neural Networks (CNN) including the following: (1) Important data, such as representative characteristics at various frequencies and orientations, may be lost due to the CNN model's usage of local receptive fields, shared weights, and subsamples. (2) To avoid over fitting, deep neural networks need a large pool of labeled training models. and (3) the higher computational overhead is yet another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of WBC cells. Framework to classify and count the leukocytes in microscopic images and developed framework supports the pathologist and boosts the CAD system accuracy. The process of analyzing leukocytes in microscopic blood images is the major concern of this research. In this thesis, first we work on WBCs segmentation from microscopic blood images. © 2024 IEEE.

关键词： Convolutional neural networks

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Blind Spot Monitoring and Detection System using Computer Vision

Blind Spot Monitoring and Detection System using Computer Vi...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： A. Venkata Ramana Yanamala Haritha B Dilli Prathap T Kaveri G Muniraju Ganesh S Vasantha Padma Department of Artificial Intelligence & Data Science Mother Theresa Institute of Engineering and Technology Palamaner Chittoor Andhra Pradesh India

ISBN: (数字)9798331537012

ISBN: (纸本)9798331537029

Blind spot accidents are a common cause of vehicle collisions, particularly during lane changes or merging into traffic. The Vision-Based Speed Detection in a Blind Spot Monitoring System addresses this issue by providing a real-time solution for monitoring blind spots, detecting objects, and estimating their speed to prevent potential accidents. This system utilizes an IP camera mounted strategically on the vehicle, such as on the side mirrors or rear bumper, to capture video footage from areas that are typically outside the driver’s field of view. The captured video feed is processed using advanced computer vision techniques powered by the YOLOv8 object detection model. The system identifies objects such as vehicles, motorcycles, or pedestrians in the blind spot and tracks their movements. Through frame-by-frame analysis, it calculates the speed of the detected objects and determines whether they pose a collision risk based on their proximity and velocity. If a hazard is detected, the system immediately triggers an audible alarm, alerting the driver to take appropriate action. The system is designed for real-time implementation in a vehicle environment. Unlike traditional systems that require external devices like laptops for processing, this paper is intended to be embedded within the vehicle using compact and cost-effective hardware, such as a Raspberry Pi or equivalent processing units. The alarm system integrates with the vehicle’s sound system or operates as an independent module to provide clear and immediate warnings. This paper represents an innovative approach to vehicle safety by combining object detection, speed estimation, and hazard assessment into a single cohesive system. Its real-time functionality ensures drivers are equipped with critical information when they need it most, enhancing road safety and reducing the likelihood of collisions. Moreover, the modular nature of the system allows for scalability, such as integration with existing Advanced

关键词： Computer vision Technological innovation Estimation Object detection Real-time systems Road safety Hardware Monitoring Vehicles Accidents

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AgriVision-B7 A Smart Agriculture Model for Disease Severity Assessment and Treatment Planning

AgriVision-B7 A Smart Agriculture Model for Disease Severity...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： K.S. Ranjith N Anusha S Ummeruman Pambala Naveen Kumar K Sandeep Moturu Dinesh Kumar Department of Artificial Intelligence & Data Science Mother Theresa Institute of Engineering and Technology Palamaner Chittoor Andhra Pradesh India

ISBN: (数字)9798331537012

ISBN: (纸本)9798331537029

Plant leaf disease detection plays a vital role in modern precision agriculture, aimed at enhancing crop yield and minimizing losses. Identifying plant diseases early and accurately is crucial for timely intervention, which helps stop the spread of diseases and reduces reliance on chemical treatments. Standard disease testing protocols base their detection on manual visual checks that are time-consuming and prone to both labor costs and personal mistakes. Computer vision and machine learning technologies enabled the creation of automatic plant leaf disease identifying systems which have addressed previous detection challenges. Deep learning methods specifically use Convolutional Neural Networks to evaluate plant leaf disease types based on their appearance. We extract essential features from high definition leaf images including shape information combined with texture components and color data through diagnosis comparison of disease and healthy leaf images. Our model is trained on extensive annotated datasets to accurately recognize diseases like blight, rust, mildew, and leaf spots. To further improve model performance, methods like transfer learning, image augmentation, and ensemble learning are incorporated.

关键词： Deep learning Plant diseases Visualization Transfer learning Feature extraction Image augmentation Ensemble learning Convolutional neural networks Diseases Testing

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Revolutionising Chrono Nutrition Using AI: A Review for Personalized Dietary Recommendation

Revolutionising Chrono Nutrition Using AI: A Review for Pers...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Vidhi Wankhede Meher Langote Prateek Verma Artificial Intelligence and Data Science Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Chrono nutrition is the science of analysing foods' effects and consumption patterns on the outer-infra-clock organism's circadian rhythms. As such, it can provide valuable possibilities for tailored health promotion and disease prevention efforts. With artificial intelligence (AI), there is an opportunity to change dietary advice and make it accurate, dynamic and personalised. This review discusses how AI can improve chrononutrition, analyse large datasets, determine the optimal circadian feeding intervals, and deliver individualised interventions based on chronobiological and genetic data. Limitations include the absence of cross-sectional analysis and inadequate consideration of socio-cultural factors in AI models. AI can significantly contribute to optimising chronotherapeutic or chrono-dietary interventions in metabolic health and disease. Furthermore, a multisectoral approach in which nutritionists, data scientists, and other actors in the healthcare sector must work together is crucial within the present state and future practical application of these advances. In extending the discourse of this paper, it is also essential to establish that much more work needs to be done for AI to make a tangible difference in chronic nutrition and translate it into an optimal solution to the problem of practical dietary advice and disease prevention.

关键词： Analytical models Sentiment analysis Reviews Prevention and mitigation Collaboration Medical treatment Genetics Synchronization artificial intelligence Diseases

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Explainable artificial intelligence in Malignant Lymphoma Classification: Optimized DenseNet121 Deep Learning Approach with Particle Swarm Optimization and Genetic Algorithm.

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IEEE Access 2025年 13卷 98639-98655页

作者： Elwahsh, Haitham Bakhiet, Ali Alirr, Omar Ibrahim Khalifa, Tarek Alsabaan, Maazen Ibrahem, Mohamed I. El-Shafeiy, Engy Applied Science Private University Faculty of Information Technology Amman 11931 Jordan Kafrelsheikh University Faculty of Computers and Information Department of Computer Science Kafrelsheikh 33516 Egypt Badya University Faculty of Computing and Data Science Egypt American University of the Middle East College of Engineering and Technology Egaila 54200 Kuwait King Saud University College of Computer and Information Sciences Department of Computer Engineering P.O.Box 51178 Riyadh 11543 Saudi Arabia Augusta University School of Computer and Cyber Sciences Augusta 30912 GA United States University of Sadat City Faculty of Computers and Artificial Intelligence Department of Computer Science Sadat City 32897 Egypt

One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence. © 2013 IEEE.

关键词： artificial intelligent deep learning genetic algorithm malignant lymphoma optimized DenseNet121 particle swarm optimization

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Performance and Security Evaluation of DBMS Models for E-Commerce and Website Session Tracking

Performance and Security Evaluation of DBMS Models for E-Com...

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Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Sathishkumar Veerappamplayam Easwaramoorthy Javid Iqbal R. Geetha Rajakumari Shaik Shabana Anjum Usha Moorthy Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamilnadu India School of Computer Science Faculty of Innovation and Technology Taylor's University Malaysia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

The exponential increase of data within businesses has a substantial impact on the performance of database management systems. As businesses deal with growing volumes of data, selecting the correct DBMS becomes critical to ensuring efficiency and data integrity. In this paper, we present our experimental insights into how different data models utilized by database management systems (DBMS) perform under varying conditions, using two scenarios: Sales Performance Analysis and Real-Time Analytics for Web Traffic. This paper examines the various data models used by DBMS, notably MySQL (Relational Model), MongoDB (Document-Based Model), Redis (Key-Value Model), Cassandra (Wide Column Model), and Neo4j (Graph Model). We evaluate the strengths and limitations of these data models in terms of data creation, manipulation, retrieval, and database security measures such as access control and data integrity for each scenario. The findings provide useful information for optimizing DBMS selection to improve data management and operational effectiveness in the face of increasing data demands.

关键词： data integrity data models database systems Real-time systems Knowledge management Communications technology Performance analysis Electronic commerce Faces Business

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks 3

End-to-End Congestion Avoidance Scheme for Underwater High S...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shivank Jain, Romil Reena, R. Kadam, Prema S. Das, Sunanda Singh, Swati Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance. © 2023 IEEE.

关键词： Community Congestion Lowering Mechanism Performance

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