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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Narayanan, Lakshmi Kanthan Loganayagi, S. Hemavathi, R. Jayalakshmi, D. Vimal, V.R. Saveetha Institute of Medical and Technical Sciences Simats Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha University Saveetha School of Engineering Department of Cse Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350384277

Industrial equipment optimization is crucial for industries that want to improve operating efficiency, reduce downtime, and cut maintenance costs. Predictive maintenance is a major strategy to achieving these goals, since it uses advanced data analytics and machine learning techniques to foresee equipment breakdowns and schedule maintenance in advance. This research investigates the use of machine learning-based predictive maintenance in the industrial sector. It discusses the approach for gathering and preprocessing data from various sensors and equipment logs, as well as the critical step of feature engineering to extract useful insights from the data. The selection of relevant machine learning algorithms and the model training method are thoroughly explored. Furthermore, the research emphasizes the importance of anomaly detection approaches in early failure detection, allowing for timely interventions to avoid equipment breakdowns. This research focuses on the creation of predictive maintenance models that estimate equipment's remaining useful life (RUL). The selection of thresholds for maintenance operations based on RUL projections is examined, with the goal of balancing cost-effectiveness and equipment reliability. The report underlines the need of continual monitoring and feedback loops in the real-time implementation of predictive maintenance systems, which will drive continued optimization. Real-world case studies demonstrate the usefulness of predictive maintenance in industrial equipment optimization, resulting in significant cost savings and improved equipment reliability. Challenges and future possibilities in this subject are also discussed, emphasizing the importance of scalable and explainable AI solutions. © 2024 IEEE.

关键词： Predictive maintenance

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Performance Metrics and Accuracy Assessment of Machine Learning Models for Crop Recommendation

Performance Metrics and Accuracy Assessment of Machine Learn...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Senthil, K. Teja, G. Srudhakerthi, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In this work, five machine-learning techniques are compared on crop recommendation. The Random Forest model achieved an accuracy of 99.09%, with both precision and recall at 99.19%. In comparison, the Decision Tree reached an accuracy of 98.33%, but demonstrated higher precision and the Support Vector Machine (SVM) also performed well but slightly less accurately than the Random Forest, with an accuracy of 97.27%. KNN showed an accuracy of 96.06% and performed well, but the ensemble approaches surpassed it. Despite being simpler and more interpretable, Logistic Regression had the worst accuracy of 95.91%. Results: These results indicate strong performance of ensemble methods on complex classification problems. Results were consistent across Decision Tree and SVM while comparable but notably weaker in KNN and Logistic Regression. The comparative analysis highlights the primacy of Random Forest on resilience and precision with an affirmation that simpler models such as Logistic Regression still have high utility in certain circumstances. Future research could focus on fine-tuning model hyperparameters, and exploring additional methodologies to improve classification performance. © 2024 IEEE.

关键词： Support vector regression

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Evaluating Sentiment Analysis Algorithms with Varied Datasets: A Machine Learning Approach 5

Evaluating Sentiment Analysis Algorithms with Varied Dataset...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Indhumathi, S. Mary Harin Fernandez, F. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Sentiment analysis is essential for comprehending public opinion on a variety of platforms and offers insightful information to researchers, businesses, and legislators. This study evaluates five machine learning algorithms-Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB) -applied to two distinct datasets: IMDB movie reviews and Twitter Airline sentiments. To provide a thorough performance comparison, each model is evaluated using recall, accuracy, precision, and F1 score. SVM achieves 91.02% accuracy on the Twitter dataset, making it the best performance;on the IMDB dataset, Logistic Regression wins with 87.39% accuracy. Naive Bayes demonstrates high precision, particularly on the Twitter dataset, though its recall score is relatively lower, indicating it is more conservative in capturing true positives. Random Forest maintains a balanced performance across all metrics, whereas KNN underperforms, particularly in handling the complexities of the IMDB dataset with a lower accuracy of 71.08%. The analysis reveals that dataset-specific factors significantly influence the performance of each algorithm, highlighting the importance of data characteristics in determining the most suitable model. While SVM excels in high-dimensional data like Twitter, Logistic Regression proves more effective with the IMDB dataset's structured text. This reinforces the need for model selection in sentiment analysis, depending on the nuances of each dataset. By providing a detailed evaluation across different models and metrics, this study contributes to optimizing sentiment analysis techniques for diverse applications, ensuring more robust and accurate predictions. © 2024 IEEE.

关键词： Tweets

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Reducing Task Scheduling Time in Cloud Computing using Novel Improved Whale Optimization Algorithm over Ant Colony Algorithm 2

Reducing Task Scheduling Time in Cloud Computing using Novel...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Priya, P. Sushma Thangaraj, S. John Justin Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

To decrease Cloud Computing Task Scheduling Time utilising Novel Improved Whale Optimisation Algorithm over Ant Colony Algorithm. A Novel Improved Whale Optimisation Algorithm schedules tasks in cloud computing. Cloud computing involves internet-based distribution of data storage, servers, databases, networking, and applications. Task scheduling data is obtained using cloudsim. We analyse, create, and implement cloud computing application framework and cloud computing infrastructures. After data import, Improved Whale Optimisation and Ant Colony Algorithm are tried. Two algorithms have two groups. Each group has 120 samples with 80% G power, 0.05% threshold, and 95% CI. Task Scheduling Time (ms) is returned for inputs. The findings come from IBM SPSS. Results indicate a significant independent sample t test (p= 0.000, p © 2024 IEEE.

关键词： Ant colony optimization

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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Revolutionizing Internet of Things with Blockchain for Enhanced Security and Data Management 7

Revolutionizing Internet of Things with Blockchain for Enhan...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Anitha, G. Gopal, K. Jain, Alok Emad, Rafal Thandra, Nagarjuna Tewari, Preeti Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Mechanical Engineeieng Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9798350350067

This paper introduces a paradigm that might transform the Internet of Things. Blockchain technology makes this system workable. Five approaches comprise the framework. Each is crucial for a powerful, automated, and scalable Internet of Things ecosystem. The framework has five techniques. Making a permanent record is the first step. This record is unchangeable. The second program handles steps. The third way secures messages. The fourth program prioritizes customer privacy. The fifth technique addresses expansion and interoperability. Comparison testing and graphic presentations reveal that the technique outperforms all key qualities. The reason is: Since reviews are detailed, the technology is supposed to transform data management and security in the fast-growing Internet of Things. This modification will likely be major. © 2024 IEEE.

关键词： Smart contract

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Changing Tides With AI-Powered Climate change Analysis

Changing Tides With AI-Powered Climate change Analysis

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Purushotham, P. Kiran, Ajmeera Anuradha, K. Reddy, Thammannagari Bhavish Vinay, Shanigarapu Manne, Naga Sai Bhargav Department of Computer Science and Engineering MLR Institute of Technology Telangana Hyderabad India CVR College of Engineering Hyderabad501510 India

ISBN: (纸本)9798350376913

Climate change represents one of the most important challenges facing humanity, and its implications extend to 2099 and beyond. However, detailed and applicable weather forecasts over many centuries are rare. This paper seeks to harness sstate-of-the-art artificial intelligence (AI) and machine learning (ML) capabilities for hundreds of years of time series climate analysis. Our method extrapolates climate numerical simulators trained to estimate future temperature scenarios based on socioeconomic emission patterns. We provide performance values in terms of out-of- sample forecasting functions and quantify emissions uncertainty as predictions extend further into the 22nd/23rd century and beyond. Additionally, weexplorethepotentialandpitfalls of AI when incorporating long-tail risks such as climate change. Our contribution provides a unique long-term perspectiveidentification of climate milestones, key decision points, and analysis of climate sensitivity over a multi- century period. We highlight ethical considerations of uncertainty diffusion and discusswaystoimproveunderstandingofclimate change likely to be experienced by very distant descendants. This paper aims to broaden and enrich conversation about humanity’s responsibility across generations beyond our immediate descendants facing profound future climate concerns. © 2024 IEEE.

关键词： Climate change

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WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

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IEEE Sensors Journal 2023年第11期23卷 12237-12250页

作者： Dande, Bhargavi Chang, Chih-Yung Wu, Shih-Jung Roy, Diptendu Sinha National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQs) of each sensor recharged. Therefore, this article proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time (AWT), and energy usage efficiency. © 2001-2012 IEEE.

关键词： computer architecture

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Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

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computers, Materials & Continua 2024年第4期79卷 351-372页

作者： Ameni Ellouze Nesrine Kadri Alaa Alaerjan Mohamed Ksantini Control and Energy Management Laboratory National Engineering SchoolPreparatory Institute for Engineering StudiesUniversity of SfaxSfax3029Tunisia Higher Institute of Computer Science and Communication Technologies University of SousseSousseBN19RHTunisia College of Computer and Information Sciences Jouf UniversityPO Box 2014SakakahSaudi Arabia

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic *** and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or ***,smartphones and their associated sensing devices operate in distributed and unstable ***,collecting their data and extracting useful information is a significant *** this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical *** the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical *** system is based on the calculation of calories burned by each user during physical *** this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical *** identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional *** human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information *** Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal *** results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and *** overcome this constraint and improve performance(96%),we proposed to use the

关键词： Human physical activities smartphone sensors deep learning distributed monitoring recommendation system uncertainty healthy calories

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Multi-Task Distillation Learning for Coffee Corticium Salmonicolor Pink Berry Disease for Real-Time Prediction 5

Multi-Task Distillation Learning for Coffee Corticium Salmon...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Raveena, S. Surendran, R. Sangeetha, M. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala engineering college Avadi Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Coffee Corticium Salmonicolor Pink Berry Disease (CCSPBD) is a substantial risk to coffee cultivation. Precise and prompt identification is essential for efficient disease treatment. This research introduces a multi-task distillation learning (MTDL) method for the real-time prediction of CCSPBD. A more extensive teacher model is trained on a comprehensive dataset of coffee plant diseases, and its knowledge is conveyed to a smaller student model tailored for CCSPBD prediction. Integrating supplementary activities about coffee plant health into the teacher model enhances the student model's comprehension of plant disease patterns. The resultant student model can precisely forecast CCSPBD in real-time, allowing farmers to implement prompt interventions to avert crop loss. Experimental findings indicate the efficacy of the suggested MTDL method in attaining high accuracy and efficiency for CCSPBD prediction. © 2024 IEEE.

关键词： Fruits

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