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International Conference on computer and Information technology (ICCIT)

作者： Neeraj Md Mokhlesur Rahman Md Lutful Kabir Sohag Kabir School of Computer Science AI and Electronics University of Bradford UK Department of Computer Science and Engineering Military Institute of Science and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The rapid surge of AI-generated content, driven by advanced large language models (LLMs) such as ChatGPT, threatens to undermine academic integrity and redefine the boundaries of plagiarism in scholarly work. This article evaluated the efficacy of five publicly available AI content detection tools in differentiating human-generated content from AI-generated ones. During the assessment, an AI-authored article was analysed using Sapling AI, Turnitin, GPTZero, Copyleaks, and Quillbot tools to assess their effectiveness in detecting AI-generated content. Following the detection reports, the paper was manually revised and resubmitted to the same tools to assess their ability to detect altered AI-authored content. This approach highlighted the susceptibility of these tools to modified texts and emphasised the difficulties in reliably detecting AI-created content. The analysis was conducted using the tools in their default state as black-box systems, without a thorough analysis of their underlying mechanisms or algorithms. Furthermore, the evaluation is confined to a single article on a specific topic, and as such, the performance of the tools assessed cannot be regarded as representative. To gain a comprehensive understanding of the effectiveness of these tools, further research and assessment involving articles from diverse disciplines and a variety of writing styles is essential.

关键词： Generative AI Large language models Plagiarism Writing Chatbots Reliability Artificial intelligence Surges Information technology Monitoring

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Smart Production and Manufacturing System Using Digital Twin technology and Machine Learning

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SN computer Science 2023年第5期4卷 1-10页

作者： Yadav, Ranjeet Roopa, Y. Mohana Lavanya, M. Ramesh, J.V.N. Chitra, N. Thulasi Babu, Gadde Raghu Electronics and Communication Engineering Government Polytechnic Daman UT Administration of Dadra and Nagar Haveli and Daman and Diu Daman and Diu 396210 India Department of CSE Institute of Aeronautical Engineering Telangana Hyderabad India Department of CSE Sri Venkateswara College of Engineering & Technology (Autonomous) R.V.S. Nagar Andhra Pradesh Chittoor 517127 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist. AP Vaddeswaram India Computer Science and Information Technology MLR Institute of Technology Hyderabad India Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering and Technology Nizampet (S.O) Telangana Hyderabad 500090 India

Adoption of digital twin (DT) in smart factories, which simulates an actual system that is manufacturing conditions and updates them in real-time, increased the output and decreased the costs and energy use which were some ways that this manifested. Fast-changing consumer demands have caused a sharp increase in factory transition in addition to producing fewer life cycles of a product. Such scenarios cannot be handled by conventional simulation and modeling techniques;we suggest a general framework for automating the creation of simulation models that are data-driven as the foundation for smart factory DTs. Our proposed framework stands out thanks to its data-driven methodology, which takes advantage of recent advances in machine learning and techniques for process mining, constant model validation, and updating. The framework's objective is to completely define and reduce the requirement for specialist knowledge to get the appropriate simulation models. A case study is used to demonstrate our framework. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Data-driven Digital twin Energy consumption Smart factories

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An Efficient Machine Learning-Based Power Management System for Smart Grids Using Renewable Energy Resources

An Efficient Machine Learning-Based Power Management System ...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： B M Kavya S Mallikarjunaswamy N Sharmila M Shilpa M Komala R Shivaji P Balachandra S Sheela G S Pavithra Dept of Electronics and Communication Engineering KS Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering JSS Academy of Technical Eduction Bengaluru India Dept of Electronics and Electrical Engineering JSS Science and Technology University Mysuru India Dept of Information Science and Engineering Bengaluru Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India Dept Electronics and Telecommunication Engineering Jagadambha college of Engineering & Technology India Dept of Mathematics SJB Institute of Technology Bengaluru India Dept of Computer Sceinec and Engineering Global Academy of Technology Bengaluru India Dept of Computer Science and Engineering JSS Academy of Technical Eduction Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In the dynamic landscape of energy systems, integrating renewable energy resources into smart grids presents substantial challenges, notably due to the inherent variability of wind and solar power. This study unveils an advanced power management system for smart grids that harnesses machine learning algorithms (ApmSGML) to optimize energy distribution and storage, ensuring stability, efficiency, and environmental sustainability. This approach is distinguished from conventional methods such as Fixed Threshold Control and Rule-Based Scheduling, which often struggle to adapt to the unpredictable nature of renewable energy sources. By leveraging predictive models, the proposed system anticipates energy production and consumption patterns, facilitating proactive adjustments to maintain supply and demand equilibrium. Additionally, A comparison of the ApmSGML’s performance against traditional power management techniques like Deterministic Load Forecasting and Static Energy Storage Management, underscoring the adaptive and continuous learning advantages offered by machine learning through techniques like deep learning and reinforcement learning. Our findings highlight significant advancements in energy efficiency and cost reduction, demonstrating the transformative potential of integrating machine learning into the energy sector.

关键词： Renewable energy sources Wind Machine learning algorithms Costs Supply and demand Power system management Storage management Energy efficiency Smart grids Standards

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Epileptic Seizure Detection using Spectral Transformation and Convolutional Neural Networks

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Journal of The Institution of Engineers (India): Series B 2022年第4期103卷 1115-1125页

作者： Cleatus, T. Saneesh Thungamani, M. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore India Visvesvaraya Technological University Bangalore India Department of Computer Science College of Horticulture University of Horticultural Sciences GKVK Bangalore India

Automatic seizure detection and classification of seizures, as well as identification of pre-ictal activity in the electroencephalogram (EEG), are extremely important in clinical research. This decreases the time it takes to identify seizures and, as a result, improves seizure activity prediction. We propose a computer-aided method to detect the pre-ictal and ictal activity from a multichannel EEG signal. Three pre-processing techniques that are applied to EEG time-domain signals to generate an image database are proposed here. This image database is given as input to the machine learning algorithm for classification. Conversion of a time domain EEG signal to an image is accomplished by extracting EEG signal features such as correlation coefficient, short-time Fourier transform (spectrogram), and mutual information. The processed EEG waveform, which is represented as images, is used to train a convolutional neural network (CNN). The CNN classifies input signals into three classes—Seizure, Normal and Pre-ictal. We used the transfer learning method, which uses Alexnet, a pre-trained CNN architecture, for image training and classification. After training on the Spectrogram, Mutual Information, and Correlation coefficient image representations of the EEG signals, we have obtained a validation accuracy of 99.33%, 95.33%, and 97.5%, respectively. © 2022, The Institution of Engineers (India).

关键词： Electroencephalography

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Improved Deep Learning Framework for Segmenting and Classifying Skin Lesions using K-nearest neighbor algorithm and Multi-Instance Learning 4

Improved Deep Learning Framework for Segmenting and Classify...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Kiran Sai, P. Nayak, Aditya Shiromani, Shikhar Hemanth, N.V.D. Dekhane, Aishwarya Patel, Nisarg T.John Institute of Technology Department of Electronics and Communication Karnataka India St. Francis Institute of Technology Department of Electronics and Telecommunication Engineering India Bits Pilani Department of Electrical and Electronics Engineering India Mahatma Gandhi Institute of Technology Department of Mechanical Engineering Gandipet India P.E.S Modern College of Engineering Department of Computer Engineering Engineering Pune India Daiict Department of Information and Communication Technology Gandhinagar India

ISBN: (纸本)9781665474368

The biggest organ that is present in the human body, which generally covers all the major body *** functions of skin in the human body are more significant, since even a little alteration in one of these functions could have an impact on other body components. Skin is more susceptible to disease and infection because it is exposed to the outside environment. Therefore, we must pay more attention to skin diseases. Lesion areas refer to the affected area of skin. The initial clinical indications of diseases like chickenpox, melanoma, etc. are skin lesions. Today, computer-aided diagnosis is more prevalent in the medical industry. For the automation of many operations in the medical profession, machine learning is crucial. It has been shown that dermoscopy, in the hands of untrained dermatologists, may actually reduce the diagnostic accuracy. There are other illnesses that are equally important in the medical community. Therefore, in addition to the diseases stated above, we also take into account two other diseases, bullae and shingles. The characteristics utilized to characterize the lesion locations are an additional crucial factor. For this, we take into account aspects of color and texture. The learning algorithms are a further topic that needs to be considered. Although there are many classifiers in use, fusion of the results of different classifiers is rapidly gaining prominence since it helps improve categorization. The final outcome the author wants is to use both the algorithms and find a novel accuracy and comapare both of them. © 2022 IEEE.

关键词： Deep learning

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DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games 2

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Abirami, P. Antony Joseph Rajan, D. Vivekanandan, T. Saillaja, V. Mohankumar, N. Rajmohan, M. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sakunthala Engineering College Vel Tech Multi Tech Dr Rangarajan Dr Department of Mathematics Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Kattankulathur Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371314

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual's performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system's influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues. © 2024 IEEE.

关键词： Convolutional neural networks

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Profit maximization of a wind-integrated deregulated system using V2G techniques and TCSC placement

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Frontiers in Energy Research 2024年 12卷

作者： Dawn, Subhojit Ramakrishna, A. Ramesh, M. Das, Shreya Shree Rao, K. Dhananjay Cali, Umit Ustun, Taha Selim Electrical and Electronics Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India Computer Science and Engineering Department Spoorthy Engineering College Hyderabad India School of Electronics Engineering VIT-AP University Amaravati India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway School of Physics Engineering and Technology University of York York United Kingdom Koriyama Japan

Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid’s ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy’s volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC’s involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer effic

关键词： Thyristors

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Machine Learning Face-Off: Performance Analysis for Breast Cancer Detection

Machine Learning Face-Off: Performance Analysis for Breast C...

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International Conference on computer and Information technology (ICCIT)

作者： Mohammed Hamza Md Mokhlesur Rahman Md Lutful Kabir Sohag Kabir School of Computer Science AI and Electronics University of Bradford UK Department of Computer Science and Engineering Military Institute of Science and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Breast cancer (BC) is the most common cancer among women, underscoring the need for accurate and reliable early detection systems. Machine learning (ML) algorithms have shown great promise in healthcare by improving diagnostic methods for various diseases. This study evaluates the performance of several ML classifiers for breast cancer detection by analyzing ultrasound images. The classifiers tested include Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Neural Networks Multi-Layer Perceptron(NNMLP). The dataset was split into various training and testing ratios. Experimental results showed that the NNMLP classifier consistently achieved the highest accuracy, particularly in the 90/10%, 70/30%, and 60/40% splits. The SVM classifier also performed well, especially in the 70/30% split. The study concluded that the 70/30% split is the most effective, yielding the highest accuracy and balanced performance across multiple classifiers. Future studies should concentrate on optimizing hyperparameters, exploring ensemble methods, and enhancing the dataset with additional data augmentation to further boost model performance and reliability in breast cancer diagnosis.

关键词： Support vector machines Accuracy Ultrasonic imaging Artificial neural networks Data augmentation Breast cancer Data models Reliability Ensemble learning Tuning

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Data Analytics for Identifying and Reducing Hospital Readmissions

Data Analytics for Identifying and Reducing Hospital Readmis...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Vidhya K Srinu N Ashok D Arun Sekar Rajasekaran Ashok Kumar S Rajasekaran U Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of AIMLDS QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering SR University Warangal Telangana India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Nathakadaiyur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Data analytics examines healthcare data to identify patterns and personalize care plans ultimately by reducing hospital readmissions. Readmissions after discharge increase healthcare costs, lower quality, and dissatisfied patients. To overcome these issues, a novel Data analytics approach has been proposed to reduce hospital readmissions. This inquiry begins with a complete dataset of clinical data, patient demographics, and Electronic Health Records (EHRs). Initially, the data gathered from the clinical data, patient demographics, and electronic health records are fed to the preprocessing module for transforming raw healthcare data into a structured form for analysis. After preprocessing, an Exploratory Data Analysis (EDA) is performed which simplifies the initial insights and hospital readmission risk factor identification. The most essential features are selected from the analyzed outcomes of EDA for readmission prediction. After feature selection, the development of hospital readmission models uses three data analytics methods. At first, the Machine Learning (ML) based methods predict the readmission status and the sequential relationships and anticipate readmission risk over time are captured using a neural network. Finally, the patient groups with comparable readmission trends are identified through hierarchical clustering. After data analytics, those outcomes are fed to the real-time patient monitoring system to reveal readmission factors such as discharge planning and timely drug management. The proposed method is validated by using healthcare datasets. In comparison, the proposed method achieves an accuracy of 97% whereas, the predictive modeling, risk factor, care process, and alert method achieves 85%, 78%, 90%, and 83% respectively.

关键词： Analytical models Data analysis Costs Accuracy Hospitals Machine learning Predictive models Feature extraction Data models Electronic medical records

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Study of Ocean Pattern's and Ocean Bed Using Applications of IOT

Study of Ocean Pattern's and Ocean Bed Using Applications of...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： K Vishwanath Rachit Reddy Sritama Roy Department of Computer Science Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

The ocean plays an integral role for influencing Earth's climate and sustaining diverse ecosystems as oceans covers almost 70 % of the earth's surface. It is important to monitor and control the ocean biodiversity through the advanced IoT technologies. The internet of Underwater things (IoUT) is an emerging technology which can connect the underwater objects as well as environment. IoUT is directly related with the navigation, disaster prediction, smart monitoring and intelligent system. Understanding the dynamics of the ocean is crucial for various applications, including weather forecasting, maritime operations, and environmental conservation. This research paper presents an in-depth analysis of an Ocean buoy Monitoring System that utilizes temperature, humidity, and geolocation data collected from buoys to predict ocean patterns, tides, and marine life. The system combines real-time sensor measurements with advanced data analysis techniques to provide valuable insights into oceanographic phenomena.

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