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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sujatha, E. Suthan, G. Sudharsan, J. Shreedhar, M. Pradhyun, P. Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

As a coastal state, agricultural instability reduces output in Tamil Nadu. Agricultural traits and features provide data that may be used to get insights into Agri-facts. With the advent of the information technology world, various highlights in agricultural sciences are being pushed to supply farmers with critical agricultural information. Machine Learning Techniques use data to build a well-defined model that assists us in making predictions. Crop forecasting, rotation, water requirements, fertilizer requirements, and crop protection are all hurdles that may be overcome. Because of the changing climatic components in the environment, it is vital to have an effective approach to promote crop growth and aid farmers in their management and output. This might assist prospective farmers in improving their farming practices. Data analytics provides the path for extracting valuable information from agricultural statistics. Crop data was evaluated, and crop recommendations were developed based on productivity and season. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Machine learning

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Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques

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Multimedia Tools and Applications 2024年第16期84卷 1-32页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Agrawal, Akshat Rani, Uma Mohan, Anand Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & amp Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering World College of Technology & ampManagement Haryana Gurugram India Department of Physics Kunwar Singh College Bihar Darbhanga India

The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily functions. While thyroid nodules are predominantly benign, certain cases may manifest malignancy, leading to thyroid cancer. Despite the generally favorable prognosis, early detection and intervention remain paramount for optimal outcomes. Given the limitations in clinical data, this paper introduces an innovative approach employing a modified XGBoost model that integrates both clinical and molecular properties into a unified predictive model. Utilizing fine-needle aspiration biopsies and gene expression analysis, our model aims to enhance cancer prediction in thyroid nodules. We explore the influence of incorporating high-quality clinical data on the prioritized gene set, employing a nature-inspired method for hyperparameter optimization. The modified XGBoost model emerges as highly recommended for non-parametric feature selection, extraction, and prediction due to its remarkable accuracy. Our proposed model attains an impressive 94.6% accuracy, surpassing other machine learning models such as C5.0 (92.5%), CART (88.7%), CHAID (83.4%), Quest (81.5%), LSVM (77%), and random tree (24.1%). The real-time efficiency of our model is demonstrated in the results section. Experiments showcase the superior performance of combined molecular and clinical data-based prediction models. Emphasis is placed on the importance of scrutinizing the origins of clinical data and maintaining vigilance over variable quality. This study signifies a substantial advancement in thyroid cancer prediction, offering a robust and accurate model that integrates diverse data sources, ultimately contributing to improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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An Ophthalmic Evaluation of Central Serous Chorioretinopathy

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computer Systems science & engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar Department of Information Technology Jeppiaar Engineering CollegeChennai600119India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

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Enhancing Transportation Safety with YOLO-Based CNN Autonomous Vehicles 1

Enhancing Transportation Safety with YOLO-Based CNN Autonomo...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Sarumathi, S. Sabir, Mohammed Suhail, Mohammed Umarulla, Mohammed Yousuf, Mohammed HKBK College Of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371802

Our project, titled Enhancing Transportation Safety with YOLO-based CNN Autonomous Vehicles', pioneers a transformative approach to autonomous driving. Through the fusion of advanced machine learning techniques, specifically the YOLO-based Convolutional Neural Network (CNN) algorithm, with meticulously selected hardware components, including Raspberry Pi 4B and IR sensors, our system addresses critical objectives such as object recognition, lane detection, and traffic signal and signboard detection, pothole detection. By leveraging diverse image datasets and optimizing the YOLO-based CNN algorithm, our system achieves exceptional accuracy and efficiency in environmental perception and navigation. Key features such as real-time object detection and precise lane detection contribute to safe navigation through complex traffic scenarios. With potential applications across automotive, logistics, and transportation industries, our project represents a significant advancement in autonomous driving technology. By prioritizing safety and efficiency, our YOLO-based CNN autonomous vehicle system holds promise for revolutionizing transportation systems and enhancing road safety worldwide. © 2024 IEEE.

关键词： Autonomous vehicles

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An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

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computer Systems science & engineering 2023年第1期44卷 281-295页

作者： R.Prabhu S.Rajesh Department of Computer Science and Engineering AAA College of Engineering and TechnologySivakasiTamilnaduIndia Department of Information Technology Mepco Schlenk Engineering College SivakasiTamilnadu India

Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet according to user *** resource allocation is vital for fulfilling user *** contrast,improper resource allocations result to load imbalance,which leads to severe service *** cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and *** extensive needs and lack of optimal resource allo-cating scheme make cloud computing more *** paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the *** proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address *** proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response *** proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing *** obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other *** evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

关键词： Cloud computing resource allocation load balance dynamic scheduling dimensionality reduction

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Futuristic Diabetes Prediction: Employing Machine Learning for Timely Diagnosis

Futuristic Diabetes Prediction: Employing Machine Learning f...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Kaviyaadharshani, D. Nivedhidha, M. Ramkumar, M.P. Emil Selvan, G.S.R. Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9798350354539

Diabetes is a prevalent circumstance that is also the core cause of numerous serious health issues, such as neurological disorders, heart attack, and stroke. So that it is placing a growing burden in the global healthcare system. The challenge lies in the accuracy of diabetes prediction model. This study centers on constructing an autonomous diabetes prediction system using a private dataset, tackling class imbalance through SMOTE techniques. Leveraging machine learning algorithms the research identifies the most effective prediction method. Feature selection algorithms guide the choice of relevant features, and the system's performance is rigorously evaluated. Remarkably, the developed model achieves impressive metrics with a success rate of 91.41%, showcasing the potential of machine learning in enhancing the prediction accuracy. These findings add vital insights to the profession, emphasizing the need of early intervention in diabetes control. © 2024 IEEE.

关键词： Forecasting

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E-Navigation: The Boon for Visually Impaired

E-Navigation: The Boon for Visually Impaired

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Patil, Leena H. Dahale, Nikhil P. Thakare, Atul L. Fursule, Sumit S. Vaidya, Anurag V. Priyadarshini-College of Engineering Computer Science & Engineering Nagpur India

ISBN: (纸本)9798350348460

Within this paper, we offer a momentous arrangement named "E-navigation: The Boon for Outwardly Disabled"pointed at revolutionizing the lives of people confronting visual disabilities. This groundbreaking framework coordinating ultrasonic sensors, GPS innovation, fire sensors, and area following to offer broad bolster to the outwardly disabled community. By utilizing ultrasonic waves and GPS, this framework empowers impediment location, real-time route, and the sharing of area subtle elements with family individuals. Furthermore, it incorporates fire sensors to upgrade security by alarming clients to potential fire dangers, especially basic for those incapable to outwardly distinguish such perils. Emphasizing reasonableness and effectiveness, this innovation points to enable people with visual impedances over a wide range. With around 39 million individuals all-inclusive hooking with visual inabilities, the potential effect of this route framework is considerable. Its execution guarantees upgraded consolation, expanded speed, and reinforced certainty in route for the outwardly impeded, cultivating more prominent freedom and cooperation in different angles of life. "E-navigation: The Boon for Outwardly Disabled"implies an inventive and promising progression balanced to altogether upgrade the lives of millions confronting challenges due to visual disability. © 2024 IEEE.

关键词： Auditory Feedback GPS Obstacle Detection Sensor Technology Ultrasonic Sensors

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A Machne Learning Framework for Compiler Optimizations 2

A Machne Learning Framework for Compiler Optimizations

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Rakesh Kumar, M. Raghav, V. Ragavendar, K. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Academic scholars have focused a lot of emphasis on the application of machine learning (ML) techniques for compiler improvements. The use of machine learning strength compilers in the general-purpose sector is still a ways off, though. MLGO1, a framework for integrating ML technology methods from the industrial compiler Low Level Virtual Machine (LLVM). In a case study, describe the details and results of replacing the LLVM in-lining-for-size optimization based on heuristics with machine-trained models. To the best of the knowledge, this study is the first extensive application of machine learning in a complex compiler pass in a real-world scenario. The primary LLVM repository has it. In contrast to the state-of-the-art LLVM-Oz, the two preferred machine learning methods, Policy Gradient and to train the in-lining-for-size model, using evolution techniques, achieve a size reduction of up to 7%. Approach improves CNN models' stability and fairness and opens the door for more dependable and trustworthy machine learning applications across a variety of sectors when combined with real-time bias detection and creative assessment techniques. It is an important strategy for addressing a number of problems in the creation and use of models, and it might greatly improve the models' equity and accuracy. Following months of rigorous research, the same model that was trained on a single corpus performs well when applied to both the same set of targets and a range of real-world targets. When using machine learning techniques in real-world situations, it is beneficial to utilize this learned model characteristic. © 2024 IEEE.

关键词： Program compilers

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Classification and Summarization of News Article using Rank SVM and Fuzzy Logic 15

Classification and Summarization of News Article using Rank ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Dhileep, A. Dharshini, T.S. Kumar, S.A. Ashok Kalaivaani, P.C.D. Sathya, S. Kongu Engineering College Computer Science and Engineering Erode India

ISBN: (纸本)9798331300579

Large amounts of unstructured data are currently trending on social media and the Web. Finding pertinent information fast from such a large unstructured corpus is difficult for individuals. Text summarizing is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarizing material by hand requires a lot of time, money, and effort. In the past, researchers have attempted to summarize the text using various machine learning techniques, but their efforts have not yet produced superior summaries. News data is a type of prepared data that can be both structured and unstructured. It contains information such as the author, date, source, location, and headline text. The approach was evaluated on CNN/Daily Mail dataset. These days, we favor the RANK SVM technique, which handles both language and text-related issues, to extract features from the textual data. This paper presents an innovative approach to news text summarization by integrating RANK SVM (Support Vector Machine for Ranking) and fuzzy logic. The system employs machine learning techniques, specifically RANK SVM, to discern and rank relevant sentences based on their importance for summarization. Fuzzy logic is then applied to enhance the processing efficiency by incorporating linguistic and contextual features such as word frequency, sentence length, and the presence of named entities. This synergistic combination aims to capture the nuances of news articles, providing a nuanced representation in the summaries. The results showcase the potential of the combined RANK SVM and fuzzy logic approach to significantly enhance the efficiency and accuracy of news text summarization, addressing the growing need for concise and informative news consumption in today's fast-paced information landscape. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

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IEEE Transactions on Control of Network Systems 2023年第3期10卷 1245-1254页

作者： Bakhshayesh, Babak Ghaffarzadeh Kebriaei, Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran School of Computer Science Tajrish *** Iran

This article presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This article also takes into account the local and the coupling/infrastructure constraints of EVs, along with transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices. © 2014 IEEE.

关键词： Electric power distribution

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