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

作者： Mothilal Nehru, G. Kudare, Roopali P. Karnawat, Amrita Tatia Rohini, J. Singh, Saurabh Nishant, Neerav SRM UNIVERSITY Faculty of Science & Humanities Department of computer Applications Chengalpattu India PCET's S B Patil Institute of Management Department of MBA Maharashtra Nigdi Pune India Indira School of Business Studies Tathawade Maharashtra Pune India Erode Sengunthar Engineering College Computer Science and Engineering Tamil Nadu Perundurai India Prestige Institute of Management and Research Department of Finance Indore India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

The need of building information technology is becoming more evident to those working in corporate financial management as the digital era arrives. An increasing number of modern societies are using enterprise resource planning (ERP) systems for their financial management needs. That way, companies can collect financial data in real-time and analyze it later. The steps of preprocessing, feature extraction, and training the model are all sequentially laid out. When it comes to statistical approaches used for data preprocessing, z-score normalization, decimal scaling, and min-max all offer very accurate results. You get the greatest results with MCML when you compare it to NCA, PCA, and Cluster-cluster LDA, the four approaches that comprise feature extraction. The suggested method employed an FTL-CSGRU strategy for model training. This method seems antiquated in comparison to the cutting-edge FTL and GRU. The statistics showed a significant improvement with an accuracy rating of 97.36%. © 2024 IEEE.

关键词： Enterprise resource planning

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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Optimizing Random Forest Algorithms for LargeScale Data Analysis 7

Optimizing Random Forest Algorithms for LargeScale Data Anal...

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

作者： Bhadula, Shuchi Adnan, Myasar Mundher Kumar, Rakesh Rana, Ajay Kaliyaperumal, Gopal Rao, Bolleddu Devananda Boob, Nandini Shirish Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350067

The exponential growth of data in recent years has necessitated the development of more efficient and scalable machine learning algorithms to handle large-scale data analysis. Random Forest (RF) algorithms, known for their robustness and accuracy, find extensive use in the realms of classification and regression. However, their performance can be significantly hindered when dealing with massive datasets due to increased computational complexity and resource demands. This paper presents a comprehensive approach to optimizing Random Forest algorithms for large-scale data analysis. We explore various strategies, including data partitioning, algorithmic changes and parallel processing to make RF algorithms more efficient and scalable. Additionally, we introduce novel techniques for feature selection and hyperparameter tuning to improve model performance. Extensive experiments conducted on multiple large-scale datasets demonstrate the effectiveness of the proposed optimizations in reducing computation time and memory usage while maintaining or even enhancing predictive accuracy. From genomics to finance, the optimised Random Forest method shows promise as a robust tool for large-scale data processing in many applications, according to the findings. © 2024 IEEE.

关键词： Data accuracy

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Leveraging Transfer Learning for Robust Medical Image Segmentation 4

Leveraging Transfer Learning for Robust Medical Image Segmen...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tyagi, Priya Sharma, Avadhesh Kumar Masson, Palak Manwal, Manika Goel, Pawan Kumar Varshney, Neeraj Nitra Technical Campus Department of CSE-AIML UP Ghaziabad India Gl Bajaj Institute of Technology and Management Electronics and Communication Engineering Uttar Pradesh Greater Noida201306 India Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University R/S Graphic Era Deemed to Be University Uttarakhand Dehradun India Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350364729

Transfer mastering is a powerful approach that has been applied to various tasks in many fields, including pc imaginative and prescient and herbal language processing. This paper describes the software of Switch gaining knowledge of medical photo segmentation, a complex project of appropriately isolating character objects from a scientific picture. This approach involves taking the parameters learned from a pre-educated deep mastering community and adapting them to a new scientific picture segmentation undertaking. The proposed method is tested on three unique datasets offering an expansion of abdominal, brain, and cardiac images. Outcomes display that the proposed technique produces higher segmentation outcomes than conventional deep getting-to-know fashions educated from scratch and might extensively reduce the time required to train and optimize those fashions. Furthermore, the proposed method can even outperform end-to-stop networks in some instances. Therefore, the proposed switch gaining knowledge of the method has the ability for used for strong medical picture segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

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Leveraging Machine Learning for Real-time Anomaly Detection and Self-Repair in IoT Devices 2

Leveraging Machine Learning for Real-time Anomaly Detection ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Ali, S. Ahamed Sujatha, D. Michael, Raj.T.F. Ramesh, G. Agoramoorthy, Moorthy Computer Science and Engineering Easwari Engineering College Ramapuram India St. Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai India Simats Department of Computer Applications Sclas TN Chennai India K.L.N. College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369960

The Internet of Things (IoT) gadgets have completely transformed a number of different businesses by producing enormous amounts of data. A machine learning framework for real-time Internet of Things anomaly detection and self-repair is presented in this research for the first time. Incorporating data preparation, anomaly detection, and selfrepair are all components of the framework. Through the use of data preparation module normalization and dimensionality reduction, Internet of Things data analysis can be improved. Through the utilization of deep neural networks, support vector machines, and several other machine learning models that have been trained on previous data, abnormal behavior can be identified. The reliability of ensemble approaches is increased. The self-repair module is responsible for automatically repairing abnormalities by resetting the settings of the device, upgrading the firmware, and applying patches. Device disruptions are reduced to a minimum by validation. Within the context of smart home and industrial automation tests, events were identified with a high degree of precision and a low percentage of false positives. Because it automatically fixes problems, the self-repair module makes the Internet of Things ecosystem better. © 2023 IEEE.

关键词： Anomaly Detection Internet of Things (IoT) Machine Learning Self-Repair

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The ForEx++ based decision tree ensemble approach for robust detection of Parkinson’s disease

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 11429-11453页

作者： Pramanik, Moumita Pradhan, Ratika Nandy, Parvati Bhoi, Akash Kumar Barsocchi, Paolo Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Department of Computer Engineering Sikkim Institute of Science and Technology South Sikkim Jorethang India Department of Medicine Sikkim Manipal Institute of Medical Sciences Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Institute of Information Science and Technologies National Research Council Pisa56124 Italy KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Burla Sambalpur768018 India

The progressive reduction of dopaminergic neurons in the human brain, especially at the substantia nigra is one of the principal causes of Parkinson’s Disease (PD). Voice alteration is one of the earliest symptoms found in PD patients. Therefore, the impaired PD subjects’ acoustic voice signal plays a crucial role in detecting the presence of Parkinson's. This manuscript presents four distinct decision tree ensemble methods of PD detection on a trailblazing ForEx++ rule-based framework. The Systematically Developed Forest (SysFor) and a Penalizing Attributes Decision Forest (ForestPA) ensemble approaches has been used for PD detection. The proposed detection schemes efficiently identify positive subjects using primary voice signal features, viz., baseline, vocal fold, and time–frequency. A novel feature selection scheme termed Feature Ranking to Feature Selection (FRFS) has also been proposed to combine filter and wrapper strategies. The proposed FRFS scheme encompasses Gel’s normality test to rank and selects outstanding features from baseline, time–frequency, and vocal fold feature groups. The SysFor and ForestPA decision forests underneath the ForEx++ rule-based framework on both FRFS feature ranking and subset selection represents Parkinson’s detection approaches, which expedite a better overall impact on segregating PD from control subjects. It has been observed that the ForestPA decision forest in the ForEx++ framework on FRFS ranked features proved to be a robust Parkinson’s detection scheme. The proposed models deliver the highest accuracy of 94.12% and a lowest mean absolute error of 0.25, resulting in an Area Under Curve (AUC) value of 0.97. © 2022, The Author(s).

关键词： Decision trees

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Scattering exceptional point in the visible

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Light(science & applications) 2023年第10期12卷 2228-2235页

作者： Tao He Zhanyi Zhang Jingyuan Zhu Yuzhi Shi Zhipeng Li Heng Wei Zeyong Wei Yong Li Zhanshan Wang Cheng-Wei Qiu Xinbin Cheng MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai 200092China Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China Shanghai Frontiers Science Center of Digital Optics Shanghai 200092China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Shanghai 200092China Department of Electronic Science and Technology Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China

Exceptional point (EP) is a special degeneracy of non-Hermitian systems. One-dimensional transmission systems operating at EPs are widely studied and applied to chiral conversion and sensing. Lately, two-dimensional systems at EPs have been exploited for their exotic scattering features, yet so far been limited to only the non-visible waveband. Here, we report a universal paradigm for achieving a high-efficiency EP in the visible by leveraging interlayer loss to accurately control the interplay between the lossy structure and scattering lightwaves. A bilayer framework is demonstrated to reflect back the incident light from the left side ( | r_(−1) | >0.999) and absorb the incident light from the right side ( | r_(+1) | < 10^(–4)). As a proof of concept, a bilayer metasurface is demonstrated to reflect and absorb the incident light with experimental efficiencies of 88% and 85%, respectively, at 532 nm. Our results open the way for a new class of nanoscale devices and power up new opportunities for EP physics.

关键词： visible exceptional incident

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data 15

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Khan, Mohammad Ahmar Verma, Pushpendra Kumar Ghaldiyal, Shivangi Balaji, N.V. Francis, G. Arun Patil, Swati Bula Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman Oman School of Computer Science and Applications IIMT University Meerut India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India IT Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab's gift to the . We provide the whole text of the news that have already been applied based on human evaluation of the articles' authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study. ©2024 IEEE.

关键词： Data collection

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