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ACS/IEEE International Conference on computer Systems and applications

作者： Vartika Bhadana Pooja Pathak Anand Singh Jalal Ashish Sharma Bhisham Sharma Imed Ben Dhaou Department of Computer Application and Engineering GLA University Mathura Department of Computer Engineering and Applications GLA University Mathura UP Department of Computer Science & Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

Recently, all have witnessed a rapid growth of COVID-19 coronavirus worldwide. The calculation of COVID-19 time-series prediction is done using many techniques like compartment models, machine learning models (ML), and deep learning models. Therefore, in this paper, the authors have proposed an ensemble-based neural network model. Neural networks (NN), along with non-linear autoregressive (NAR) functions, fitting neural networks (FITNET), or fuzzy Systems, are widely used in time-series forecasting. The responses of NAR, FITNET predictor modules are aggregated using fuzzy logic, which improves the final prediction by intelligently integrating outputs of different modules. The whole model was put to the test in terms of forecasting the coronavirus time series in India, at 13 States. In the validation data set, results of ensemble NN models with fuzzy response integration demonstrate extremely better-predicted values. Overall, the results reveal that a modular neural network with fuzzy (MNNF) beats all other approaches in performance metrics, like Root Mean Squared Error (RMSE). Prediction errors of ensemble NN i.e., MNNF are much smaller than those of classic monolithic neural networks, showing advantages of the method proposed. The model provides the prediction for the upcoming 8 days.

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Research on Improved Quick Sort Algorithm with Duplicate Value Handling

Research on Improved Quick Sort Algorithm with Duplicate Val...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Hemin Amit Yadav Asif Khan Abhishek Pratap Sah College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

An improved algorithm based on Quick Sort algorithm research method is proposed to deal with prevailing duplicate values in the sorting of data. The duplicate values are specially processed, which effectively reduces the number of division and greatly reduces the number of recursions. The experimental results show that the time efficiency is improved by more than 50% when dealing with large data than the traditional Quick Sort algorithm without reducing the space efficiency.

关键词： Distributed processing Multicore processing Big Data Stability analysis Hardware Distributed computing Optimization Sorting

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Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics

Utilizing Random Forests for High-Accuracy Classification in...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Meenakshi Maindola Ramy Riad Al-Fatlawy Rakesh Kumar Nandini Shirish Boob S P Sreeja N Sirisha Abhinav Srivastava Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Computer Engineering & Applications GLA University Mathura Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Master of Computer Application New Horizon College of Engineering Bangalore Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana FS University Shikohabad Firozabad India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results.

关键词： Radio frequency Support vector machines Accuracy Scalability Robustness Classification algorithms Medical diagnosis Random forests Medical diagnostic imaging Diseases

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Disease Detection in C4 Plants using Machine Learning Approaches

Disease Detection in C4 Plants using Machine Learning Approa...

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

作者： Souravdeep Singh Geeta Sharma Ram Kumar Adesh Kumar School of Computer Application Lovely Professional University Punjab Phagwara India School of Computing Science and Engineering VIT Bhopal University Bhopal India School of Agriculture Lovely Professional University Phagwara India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Disease detection in crops is essential to maintaining food security and agricultural productivity. Plant disease identification is essential to maintaining crop health and increasing agricultural productivity. with the advancement of computational techniques like Machine learning is used for extracting insights from data, enabling automation, and making predictions in various farming domains. Recent assessments reveal that infections that develop on the leaves of plants are causing significant losses for 56% of the agriculture sector. Monitoring the disease's growth and increasing agricultural production is essential. We must first identify the condition early on and take action to stop it from propagating. Therefore, we could potentially able to resolve this challenge by implementing some algorithms for identifying diseases on leaflets. In this study, we investigate the efficacy of Convolutional Neural Networks (CNNs) for detecting diseases in two major crops: maize and sugarcane which lie under C4 plants. Utilizing a dataset located from Kaggle, comprising a diverse range of images depicting various disease symptoms, we trained and evaluated CNN models for both crops. The accuracy of disease detection achieved remarkable results, with 98% accuracy for maize and 100% accuracy for sugarcane. Our comparative analysis highlights the efficacy of CNNs in accurately identifying diseases in these crops, showcasing their potential for aiding farmers in timely disease management and enhancing agricultural yield.

关键词： Productivity Accuracy Plants (biology) Crops Food security Convolutional neural networks Sustainable development Diseases Farming Resilience

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Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means

Behavioral Time Management Analysis: Clustering Productivity...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Winster Praveenraj, D David Habelalmateen, Mohammed I. Shrivastava, Akrati Kaur, Amarpreet Valarmathy, A.S. Patnaik, Chintamani Prasad Christ University School of Business and Management Bangalore India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Institute of Technology and Management Department of Mba Andhra Pradesh Tekkali India

ISBN: (纸本)9798350390759

This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time

关键词： Information management

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AI for Disaster Management: Predictive Models and Response Strategies

AI for Disaster Management: Predictive Models and Response S...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： A. Shameem Devupalli Saroja Damodar S. Hotkar Bijaya Bijeta Nayak Anand Pandey Sachin Tripathi AMET Business School AMET University Department of Computer Science and Engineering Vignan's Institute of Information Technology (A) Visakhapatnam Department of Computer Science and Engineering R.T.E. Society's Rural Engineering College Hulkoti Karnataka School of Mechanical Engineering KIIT Deemed to be University Bhubaneswar Odisha Computer Science and Application Sharda University Greater Noida U.P India Symbiosis Law School Nagpur Symbiosis International (Deemed) University (SIU) WATHODA Nagpur Maharashtra India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The application of artificial intelligence (AI) in managing severe weather events has become more necessary in recent years owing to their increased frequency. Disaster management is required to control and avoid such occurrences. To successfully aid in better and quicker reactions to catastrophes, AI is widely employed in prediction and disaster preparation, damage mitigation and minimization, and rescue phases. To determine how AI technology can be used to lessen the damaging impacts of different disasters, this article looks into the potential for connecting artificial intelligence methods based on technological innovation. The study potentially contributes in designing an effective framework for disaster management based on the predictive accuracy and response time.

关键词： Technological innovation Storms Disasters Prevention and mitigation Disaster management Predictive models Software Time factors Personnel Artificial intelligence

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An Analysis of Encapsulating Security Payload in Wireless Network Security 1

An Analysis of Encapsulating Security Payload in Wireless Ne...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Verma, Devika A. Singh, Vaishali Beemkumar, N. Shree, Ritu Minhas, Deepak Yuvaraj, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350359756

The Encapsulating Safety Payload (ESP) is a necessary part of the IPsec suite of protocols, imparting a critical layer of security within a Wi-Fi community. This paper aims to evaluate ESP and its software in wireless community protection comprehensively. ESP capabilities as a secured payload encapsulating data via imparting integrity, authentication, and confidentiality. It commonly accomplishes this by using a mixture of cryptography and tunneling to shield the contents of the packets from alteration, eavesdropping, and unauthorized get entry. Moreover, this paper outlines the distinctive ESP modes and cipher suites, presenting how they may be used to defend data being despatched over a Wi-Fi community. ESP additionally affords numerous safety enhancements, such as protection from replay assaults, packet length control, and critical negotiation. Furthermore, this paper explores the challenges associated with implementing and deploying ESP in wireless networks. Subsequently, the paper will pay attention to the numerous overall performance issues for ESP utilization by analyzing the effect of latency, throughput, and power intake on ESP-enabled Wi-Fi networks. © 2023 IEEE.

关键词： Authentication

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Efficient and Privacy-Preserving Deep Inference Towards Cloud-Edge Collaborative

SSRN

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SSRN 2023年

作者： Wang, Yulong Meng, Fanzhi Deng, Kai Yang, Run Xin, Bangzhou Yin, Mingyong Institute of computer application China Academy of Engineering Physics Mianyang China School of Cyberspace Science Harbin Institute of Technology Harbin China

The cloud-edge collaborative inference approach splits deep neural networks (DNNs) into two parts to run collaboratively on resource-constrained edge devices and cloud servers, aiming at minimizing inference latency and protecting data privacy for time-critical computing system. However, despite not exposing the raw input data from edge devices directly to the cloud, state-of-the-art attacks can still target collaborative inference to reconstruct the raw private data from exposed local models' intermediate outputs, introducing serious privacy *** this paper, we propose a secure privacy inference framework for time-critical cloud-edge collaboration system, called CIS (Collaborative Inference Shield), which supports adaptively partitioning the network according to dynamically changing network bandwidth and fully releases the computational power of edge devices. To mitigate the influence introduced by private perturbation, CIS provides a way to achieve differential privacy protection by adding refined noise to the intermediate layer feature maps offloaded to the cloud. Meanwhile, given a total privacy budget, the budget is reasonably allocated by the size of the feature graph rank generated by different convolution filters, making cloud inference robust to the perturbed data, thus effectively trading-off between privacy and ***, we construct a real cloud-edge collaborative inference computing scenario to verify the effectiveness of inference latency and model partitioning on resource-constrained edge devices. Furthermore, the state-of-the-art cloud-edge collaborative reconstruction attack is utilized to evaluate the practical availability of the end-to-end privacy protection mechanism provided by CIS. © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Extrapolative Preservation Management of Medical Equipment through IoT

Extrapolative Preservation Management of Medical Equipment t...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Rajeev Tripathi Vinay Kumar Mishra Himani Maheshwari Raj Gaurang Tiwari Ambuj Kumar Agarwal Ashulekha Gupta Department of Computer Application and Sciences School of Management Sciences Lucknow India School of Computer Application Babu Banarasi Das University Lucknow India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

The healthcare industry is primarily driven by technological innovations as they are a significant source of inspiration to provide the best patient care. To provide patients with prompt, high-quality care, medical device manufacturing has grown at an unprecedented rate. Hospitals should use the best maintenance techniques to increase the life of their equipment while trying to reduce maintenance costs and efforts as medical device usage increases. This paper proposes a predictive maintenance method (PdM) to assist in identifying critical equipment failures with diverse and frequent failure modes. The proposed method is based on the understanding of the physics of the incident, real-time data collection of relevant parameters using Internet of Things (IoT) technology, and application of machine learning algorithms to predict and classify the condition of the device as good and bad. To demonstrate the feasibility and effectiveness of moving from traditional maintenance to PdM, an economic analysis should be provided. The objective of this study was to determine the challenge of medical device maintenance using an Internet of Things (loT) enabled automated health monitoring system for devices that generate large amounts of data. real-time data, at scale, in healthcare organizations.

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Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

TechRxiv

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TechRxiv 2023年

作者： Sen, Sohom Maiti, Samaresh Manna, Sumanta Roy, Bibaswan Ghosh, Ankit Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India School of Nuclear Studies and Application Jadavpur University Kolkata India

Intelligent and Smart aquaculture is nowadays one of the most important latest development trends in the field of the aquaculture industry to overcome the problems of the farmers due to water quality monitoring, food feeding, temperature imbalance and recycling of water. Smart water quality prediction in the real-time environment using intelligent machine learning models and IoT sensors establish the basis for the evaluation, planning and intelligent regulation of the aquaculture environment. The purpose of this study is to propose an intelligent Machine learning and IoT-based Biofloc system that enhance the efficiency, production, water recycling system and automatic food feeding system. This paper proposed a system that collects process data from sensors, store data in the cloud, and analyses it using different latest machine learning models such as a Decision tree classification model, and Random Forest Model to predict the water quality and provides real-time monitoring through an android app. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed linear regression, Random Forest and other ML models have been implemented and tested to validate and achieve a satisfactory result. A performance comparative analysis of the different ML algorithms has been conducted based on a few metrics such as accuracy, recall, precision and F1-score. Random Forest Classifier has surpassed all the other models in terms of accuracy, recall, precision and F1 score. Random Forest Classifier has exhibited an accuracy of 73.76%, recall, precision and F1-score of 90%, 75% and 82% respectively. © 2023, CC BY.

关键词： Aquaculture

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