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Electricity Demand Prediction through Artificial Intelligence Methods

Electricity Demand Prediction through Artificial Intelligenc...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Neeraj Mittal, Tanusha Al-Jawahry, Hassan M. Bansal, Saloni Deepak, A. Deepika, N.M. Kaushik, Rohit Department of Electrical Engineering Doon Institute of Engineering and Technology Uttarakhand Rishikesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Engineering and Applications GLA University Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Institute of Aeronautical Engineering Dundigal Hyderabad India Faculty of Management SRM Institute of Science and Technology NCR Campus India

ISBN: (纸本)9798350355338

Recently accurate predictions of electricity demands have emerged from the Liberalization and commercialization of the energy area. The future energy or intelligent grid market. A better request-side management method and a shift of mindset are required for a more credible system scale estimate of up to one year. However, electricity is difficult to forecast as elements, including sociological and environmental activity and climatic influences, are affected. The two methodologies utilized to predict cargo, in general, are artificial intelligence and analytical procedures. All analytical methodologies are frequently used in regression literature: linear regression, Box-Jenkins’ methodology, and nonparametric methods. The analytical approaches function effectively in everyday settings regarding meteorological, sociological, and psychological problems but don’t give satisfying outcomes. Consequently, the period of time does not change on the basis of economic developments. Inference systems tactics are among the artificial intelligence systems. © 2024 IEEE.

关键词： Demand response

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Lane Detection for Autonomous Vehicles with Canny Edge Detection and General Filter Convolutional Neural Network

Lane Detection for Autonomous Vehicles with Canny Edge Detec...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： S Kishor Rekha R Nair Tina Babu S Sindhu S Vishnu Vilashini Department of Computer Applications Nitte (Deemed to be University) Mangalore India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India

Lane detection has been a complex issue that has garnered the attention of the computer vision community for many years. It is a crucial element for self-driving cars and computer vision in general. Lane detection is used to define the path for autonomous vehicles and prevent the risk of drifting into another lane. So proposed a study to develop a method that can detect lane lines in real-time using the OpenCV library and computer vision concepts. To accomplish this, identify the white markings on both sides of the lane. The proposed work focuses on identifying the road lane lines that autonomous cars must adhere to, ensuring that they do not cross into other lanes or drive in the opposite direction, which could lead to accidents. The performance of the work assessed using actual road images and videos captured by the car’s front-mounted camera. So proposing a basic and easy algorithm for tacking and detecting lanes. The lanes have been detected through all competitions based on data from the camera, which is carried by algorithms. The proposed work implementing image processing techniques to capture the exact and accurate lane ways and hough transformation techniques.

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Comparative Analysis of Deep Learning Algorithms for Image Recognition in Medical Imaging 2

Comparative Analysis of Deep Learning Algorithms for Image R...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Ashokkumar, S. Kumar, S. Magesh Sugumaran, D. Rajaraman, R. Saranya, N. Naga Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Tamil Nadu Chennai India

ISBN: (纸本)9798350383867

This research paper compares deep learning models through the imaging tasks of medical imaging. Modern-age architectures such as CNNs i.e., AlexNet, VGG, Resnet, Densensnet, and U-Net are the foundations of state of the art medical image analysis. They are applied to various tasks like detection of cancer, organ segmentation, and classification of diseases among others. Reports indicate that there are specific networks of neurons which perform optimization for specified applications in this sense of which the ResNet architecture is better than others in tumor detection and U-Net is better than SegNet in organ segmentation tasks. Leveraging the transfer learning techniques trained on the ImageNet, our approach to disease classification succeeded;in addition, we utilized ensemble methods that combined the predictions from multiple models, which were pretty effective. This research explores the choice and fine tuning deep learning strategies for medical image analysis and deals with a huge push to diversify imaging techniques diagnostics and treatment planning. ©2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Brain Tumor Detection Using Convolutional Neural Networks in Medical 1

Enhancing Brain Tumor Detection Using Convolutional Neural N...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Rishav Gund, Ms Pratima Tarambale, Manoj Shrivastava, Kapil Choudhari, Ashwini Suresh Dumne, Neha Baburao Manipal University Jaipur Department of Computer Science Jaipur India Pune India PVG's Coet and Gkp Iom Electrical Engineering Department Maharashtra Pune411009 India Department of Computer Engineering and Applications GLA University Mathura India Dhole Patil College of Engineering Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798350330915

It is commonly known that early and accurate brain tumour detection is critical, which emphasizes the need for better diagnostic techniques that improve patient outcomes. Although they are the mainstay of diagnosis, conventional imaging methods like CT and MRI scans are frequently limited by their requirement for extreme precision. In order to improve brain tumour detection in medical imaging, this study explores the effectiveness of convolutional neural networks, or CNNs, with a particular emphasis on the U-Net and DenseNet architectures. Through the use of a complex methodology that involves extensive validation procedures, rigorous model training, and data augmentation, the study shows a significant improvement in the accuracy of brain tumour detection. The results confirm that CNNs outperform conventional techniques in more accurately determining and categorizing brain tumour kinds and margins. This development clarifies the significant influence of deep learning in neuro-oncology, pointing to a crucial shift toward AI-enhanced diagnostic methods in clinical settings and establishing the framework for further study in more extensive and diverse datasets © 2023 IEEE.

关键词： Diagnosis

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A hybrid approach for medical images classification and segmentation to reduce complexity

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Innovations in Systems and Software engineering 2023年第1期19卷 33-46页

作者： Kumar, Ankit Bhatia, Surbhi Bhardwaj, Rajat Singh, Kamred Udham varshney, Neeraj Raja, Linesh Department of Computer Engineering and Applications GLA University Chaumuhan Uttar Pradesh Mathura281406 India Department of Information Systems College of Computer Science and Information Technology King Faisal University Dammam31451 Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Chandigarh-Patiala National Highway Punjab Rajpura140401 India Department of Computer Science and Information Engineering National Cheng Kung University Dasyue Street Tainan Tainan701 Taiwan School of Computing Graphic Era Hill University Bell Road Uttarakhand Dehradun248002 India Department of Computer Apllications Manipal University Jaipur Rajasthan Jaipur303007 India

The computational domain facilitates the performance of novel and innovative medical research and development tasks by providing support and computational power. This analysis method forecasts the future by analyzing the data we now have. The method may be divided into three primary phases: preprocessing, feature extraction, and classification. The research presented here aimed to improve the precision with which heart disease could be predicted across three distinct phases. The first step is thoroughly examining the databases kept at the UCI computer repository. In this study, we use the dataset’s five different algorithms, decision tree, Naive Bayes, random forest, KNN, and support vector machine, to compare their respective performances. In addition to age, the suggested revolutionary technique considers other characteristics such as pulse rate, cholesterol, and so on, which was not the case in earlier studies. In the past, age was the primary consideration in analysis and illness prediction. Compared to more traditional methods, improved prediction accuracy is achieved by modifying the study’s primitive properties. Third, this research introduced a novel hybrid classification model by fusing support vector machines and k-nearest neighbor classification techniques. A k-nearest neighbor classifier will do the heavy lifting to classify the data, while support vector machines will extract the dataset’s features. The accuracy rates for the various prediction methods decision tree, KNN, Naive Bayes, random forest, support vector machine, and proposed method range from 72.53% to 87.32% to 87.39% to 81.34%, respectively. The new technique decreases execution value by 5 % and increases accuracy by up to 8 %. The suggested model outperforms state-of-the-art approaches in terms of accuracy and implementation speed. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Decision trees

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning

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Procedia computer science 2025年 252卷 241-250页

作者： Amit V Kachavimath Narayan D G Department of Master of Computer Applications KLE Technological University Hubballi 580031 India School of Computer Science and Engineering KLE Technological University Hubballi 580031 India

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOVA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model.

关键词： SDN DDoS Attacks Machine Learrning

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AI-Powered Job Market Analysis: Transforming Workforce Trends and Employment Predictions 5

AI-Powered Job Market Analysis: Transforming Workforce Trend...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Purohit, Sanjana Shuja, Mirza Walia, Sahil Kumar, Mukesh Bajaj, Karan Bhardwaj, Vivek Jecrc University School of Computer Applications Rajasthan Jaipur India Lovely Professional University School of Computer Application Punjab Phagwara India Snowflake Inc. AtlantaGA United States Chandigarh Group of Colleges Chandigarh School of Business Department of Computer Application Punjab Mohali140307 India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350376685

This analysis delves into the "AI-Powered Job Market Insights"dataset, offering a comprehensive exploration of how artificial intelligence (AI) and automation are influencing the modern job market. The dataset provides a snapshot of job listings across various industries, highlighting factors such as AI adoption levels, automation risk, and salary ranges. By examining these elements, we aim to uncover key trends and insights into how AI is reshaping employment opportunities and job market dynamics. The primary objective of this analysis is to identify and understand key patterns related to salaries, required skills, remote work, and job market trends influenced by AI. By employing various analytical techniques, we seek to reveal how AI adoption impacts compensation, skill requirements, and job growth, as well as how it correlates with the likelihood of remote work. © 2024 IEEE.

关键词： Job analysis

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Big Data and the Internet of Things as Enablers for Business Digital Transformation Efforts 4

Big Data and the Internet of Things as Enablers for Business...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Gomathi, B. Jannepally, Abbinaya Reddy Ghansela, Siddharth Sujatha, R. Kashyap, Amit Kumar Vidhya Shri Shankarlal Sundarbai Shasun Jain College for Women Department of Computer Science Chennai India Solutions Architect India Gb Pant Institute of Engineering and Technology Department of Computer Science and Application Uttarakhand India Psg College of Arts & Science Department of Information Technology Coimbatore India Nirma University Institute of Law Ahmedabad India Shri Shankarlal Sundarbai Shasun Jain College for Women Department of Computer Applications Chennai India

ISBN: (纸本)9798350307757

This comprehensive study delves into the transformative roles of Big Data and the Internet of Things (IoT) in facilitating digital transformation in businesses. It explores how the synergy between these technologies drives operational efficiency, enhances customer engagement, and fosters innovation. The research employs a mixed-method approach, including literature reviews, case studies, and empirical data analysis, to assess the impact of Big Data and IoT on business strategies and performance. The findings indicate that while Big Data and IoT significantly contribute to digital transformation, challenges like data security and infrastructure requirements need addressing. This study provides valuable insights for businesses aiming to leverage these technologies for competitive advantage. © 2024 IEEE.

关键词： Internet of things

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Machine Learning and Artificial Intelligence in Cybersecurity: Innovations and Challenges

Machine Learning and Artificial Intelligence in Cybersecurit...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Vinayak Khatri Gaurav Agarwal Amit Kumar Gupta Akash Sanghi Department of Computer Science and Engineering Invertis University Bareilly India Department of Computer Applications KIET Group of Institutions Ghaziabad India Department of Computer Applications Invertis University Bareilly India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Rapid developments in machine learning (ML) and artificial intelligence (AI) have had a big impact on cyber security by providing creative answers to challenging security problems. This analysis examines how AI and ML are integrated into cyber security, emphasizing the uses, advantages, and drawbacks of each technology. To improve threat detection, malware categorization, phishing prevention, and network security, this study analyses many AI and ML methods, such as reinforcement learning, deep learning, supervised learning, and unsupervised learning. According to recent study findings, AI-driven intrusion detection systems can detect intrusions with up to 99% accuracy, which greatly lowers the number of false positives. Comparatively, compared to conventional techniques, machine learning algorithms for malware detection have increased detection rates by 20-30%. However, several technological obstacles stand in the way of AI's smooth integration with cyber security. These obstacles include problems with data quality, adversarial assaults, interpretability of models, and the computing demands of implementing AI at scale. There are other major obstacles, such as privacy and ethical considerations. Nonetheless, issues including adversarial attacks, ethical dilemmas, and poor data quality persist. This paper illustrates the usefulness of AI and ML technologies in cyber security through real-world case studies. Lastly, it emphasizes how AI and ML have the ability to revolutionize cyber security procedures by highlighting upcoming trends and research paths. This review attempts to provide useful insights for researchers, practitioners, and policymakers to harness AI and ML for a safer digital environment by providing a thorough overview and highlighting important discoveries.

关键词： Deep learning Accuracy Prevention and mitigation Phishing Data integrity Supervised learning Intrusion detection Malware computer crime Unsupervised learning

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Comparison of Optimizers for Lung Cancer Classification using Deep Convolution Neural Network

Comparison of Optimizers for Lung Cancer Classification usin...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： R. B. Dev S. Shanthi V. Brindha T. Arunkarthi K. Ashwinth R. Kuppuchamy Department of Computer Science and Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

The process of identifying and categorizing lung cancer in its early stages is difficult, yet doing so will improve patient survival rates. There is a wealth of research that segments and categorizes lung nodules using convolution neural networks (CNN). Numerous factors, including batch size, epochs, learning rate, optimizers, activation function, and weight initialization, affect the model's performance. To categorize CT scan images as benign, malignant, or normal, a deep CNN model is developed. Optimizers are crucial to the training process of the model because they adjust the weight and learning rate of the network in each epoch, which lowers loss and raises classifier accuracy. Using ReLU and selu as activation functions, this work offers a thorough analysis of the performance for a number of optimizers, including Nesterov Adaptive Momentum (Nadam), Adaptive Delta (Adadelta), Adaptive Max Pooling (Adamax), Stochastic Gradient Descent (SGD), Adaptive gradient (Adagrad), Adaptive Momentum (Adam), Adaptive Gradient Descent (Adagrad), and Root Mean Square Propagation (RMSProp)[1][1][1][1][1][1]. The best training accuracy for the ReLu activation function is 92.67%, with a validation accuracy of 97.92, and for the Selu activation function, it is 95.42%, with a validation accuracy of 96.88, according to the Adam optimizer. The study aims to assess various optimization techniques in deep CNNs for lung cancer classification using CT scan images. It analyzes different optimizers to understand their impact on model convergence, accuracy, and generalization. This insight is crucial for selecting the most effective optimization methods in lung cancer diagnosis.

关键词： Training Convolution Computed tomography Computational modeling Neural networks Memory management Lung cancer

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