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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Khakzad, Seyedeh Fatemeh Firozjaee, Tahere Taghizade Mortazavi, Reza Khoshnevisan, Erfan Khoshnevisan, Sina Shahrood University of Technology Faculty of Civil Engineering Shahrood Iran School of Engineering Damghan University Damghan Iran Shahrood University of Technology Faculty of Computer Science Shahrood Iran

ISBN: (纸本)9798331532543

As the importance of solar energy continues to grow as a pivotal renewable resource, the accurate identification of solar panels within satellite imagery becomes indispensable for both resource assessment and strategic planning. The increasing utilization of solar power signifies a shift towards cleaner and renewable energy sources and serves as a critical measure in mitigating the adverse impacts of climate change, which is predominantly fueled by the widespread use of fossil fuels. This study employs a high-quality, manually labeled dataset comprising 250 satellite images of solar panels in California. Four convolutional neural network (CNN) architectures - DeepLabV3+ VGG16, ResU-Net, UNet3+, and SegNet - are evaluated for their efficacy in detecting solar panels. The models were trained for 50 epochs using a learning rate of 0.0003, the Binary Cross-Entropy loss function, and the Adam optimizer. Performance was assessed using the Dice Coefficient, Recall, and Precision metrics, with DeepLabV3+VGG16 achieving the highest performance with a Dice Coefficient of 94.62% and Recall of 94.23%. These findings provide valuable insights for researchers in selecting appropriate network architectures for accurate semantic segmentation tasks and effective solar panel detection and estimation. © 2024 IEEE.

关键词： Clean energy

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Election Prediction Based on Sentiment Analysis using Twitter Data

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International Journal of engineering, Transactions B: Applications 2022年第2期35卷 372-379页

作者： Yavari, A. Hassanpour, H. Cami, B. Rahimpour Mahdavi, M. Faculty of Computer Engineering and IT Shahrood University of Technology Shahrood Iran Faculty of Computer and IT Engineering Mazandaran University of Science and Technology Babol Iran Sydney International School of Technology and Commerce Sydney Australia

Election prediction has always been of interest to many people. In the last decade, an increasing influence of social networks and the possibility of sharing opinions and ideas has rendered election prediction based on social network data analysis. This paper, drawing on Twitter data and sentiment analysis, uses the proportion of positive messages rate to negative messages rate as an effective indicator for predicting elections. Then, using the aging estimation method, itpredicts the values of this indicator in future time windows. The experiments conducted on Twitter data related to the 2020 United States presidential election in a four-month time window indicate that the indicator values and eventually the election results can be predicted with high accuracy. © 2022 Materials and Energy Research Center. All rights reserved.

关键词： Sentiment analysis

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IoT-Based Healthcare Monitoring System 1

IoT-Based Healthcare Monitoring System

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hadi, Marwa Abdulrahman Al-Gaphari, Ghaleb Hamoud Al-Baltah, Ibrahim Ahmed Julian, Francisco Bacallao Al-Hadi, Ahmed Abdulrahman Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Sana’a Yemen Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Cardiac-Center Military Hospital Yemen Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (纸本)9798331533557

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions. © 2024 IEEE.

关键词： Electronic health record

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A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services

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Multimedia Tools and Applications 2023年第29期82卷 44783-44800页

作者： Chowdhary, Sunil Kumar Rao, A.L.N. Faculty of Computer Science and Engineering Dr. A P J Abdul Kalam Technical University Uttar Pradesh Lucknow India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Noida India

Some of the conventional approaches to task scheduling are discussed in this section. The scheduling of tasks in the Internet of Things (IoT) application is a complex job in cloud computing due to the heterogeneity characteristics of the IoT. Therefore, an efficient scheduling approach is vital in improving the performance of the Cloud-IoT applications. The improvement in QoS mainly depends on efficient task scheduling and allocating the task to the appropriate Virtual Machine (VM). This paper uses a clustering-based approach to design an optimal solution for task scheduling and VM allocation for IoT applications in education services. The workflow is simplified with horizontal and vertical clustering, and VM is allocated. The task is classified into coarse and fine-grained, based on the average execution time of the total number of tasks. The average execution time is calculated based on the best and worst performance of VM. The vertical clustering approach clusters the tasks into sequential and parallel, thereby turning the huge volume of tasks into simple ones. The horizontal clustering approach selects the VM with the help of a bipartite matching graph which enhances the Quality of Service (QoS). The proposed task clustering has been evaluated in metrics like makespan, response time, throughput, total execution time, task completion time, and VM utilization. The proposed approach achieved a makespan of 2500 ms and throughput of 83 Mbps for 100 tasks, a response time of 0.12 ms, a total execution time of 640 ms, and a total task completion time of 340,000 ms for a 2 arrival rate based on performance evaluation results. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Quality of service

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A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs)

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International Journal of Information technology (Singapore) 2024年第3期16卷 1429-1435页

作者： Barve, Amit Pallavi, R. Deepak, Shashikant Murugan, R. Yadav, Dhananjay Singh, Ashutosh Kr. Sharma, Meenakshi Shalini, S. Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Karnataka Bangalore India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology UP Noida India Department of Education Sanskriti University UP Mathura India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India

Security, heterogeneity, and interoperability are the prominent concerns in wireless sensor networks (WSNs) with restricted amounts of resources such as energy, transmission range, and processing of packets. Moreover, secured and trusted communication among WSNs is required to enhance network lifetime and security, thereby reducing energy consumption in the network. Thus, the given paper presents an ontological-based trust aware hybrid key management scheme (OTAHKMS) for WSNs. The proposed approach is a two-fold approach, where the first phase involves the encryption process for maintaining data confidentiality and integrity, while the second phase involves the use of ontology for capturing trust relationships and representing contextual information among several nodes in the network. The performance of the proposed trusted approach is validated and compared with existing cited works based on evaluation metrics such as communication trust value (CTV), network throughput (NT), packet delivery ratio (PDR), and energy consumption (EC). The results show that the proposed ontological-based trusted approach efficiently outperforms existing protocols, thereby enhancing network lifetime and energy usage in WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Encryption Packets transmission Security Trust validation Wireless sensor networks (WSNs)

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A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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computer Systems science & engineering 2023年第9期46卷 3239-3260页

作者： K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala Department of Computer Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

关键词： Data mining crop prediction k-prototypes k-means cluster machine learning

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A Review of Social Media Sentiment Analysis Using Machine Learning To Enhance Stock Market Prediction 2

A Review of Social Media Sentiment Analysis Using Machine Le...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Rohini Saratkar, Saniya Thakre, Gayatri Thute, Trupti Chaudhari, Aarti Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Predicting stock prices is a critical financial activity that is frequently impacted by sentiment analysis, machine learning, and data mining approaches. This examination looks at a variety of stock price prediction methods, including artificial neural networks, graph-based approaches, time series analysis, and classic machine learning techniques such as Support Vector Machine, K-Nearest Neighbor, and Random Forest. The article assesses the problems and efficacy of various methodologies, including a thorough examination of sentiment analysis in social media to enhance stock market predictions. By examining multiple research, this study provides a complete evaluation of various prediction algorithms and reveals their shortcomings, such as overfitting, non-stationary data, and the complexity of financial transactions. It helps academics and practitioners construct more accurate and dependable stock forecasting models by merging financial data with sentiment analysis. The study also discusses rising trends and new potential for further study, especially hybrid models and advanced deep learning approaches to improve the accuracy of stock forecasts. © 2024 IEEE.

关键词： Prediction models

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An Innovative Method of Developing BI-LSTM and KNN Algorithms for Disintegrating Sentiments Through Reviews 4

An Innovative Method of Developing BI-LSTM and KNN Algorithm...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Naveen, V. Nandhini, T.J. Kumar, T. Rajesh Aggarwal, Alankrita Brayyich, Mohammed Al-Taee, Fay Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Apex Institute of Technology Chandigarh University Computer Science and Engineering India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

During the COVID-19 pandemic, schools globally transitioned to online education due to lockdowns, prompting concerns about its acceptance and effectiveness among stakeholders. This study analyzes public sentiment towards e-learning using a dataset of 17,155 tweets collected from social media platforms like Facebook, Instagram, and Twitter. Machine learning and deep learning techniques are employed for text analysis, including sentiment analysis using Senti Word Net, Text Blob, and VADER. Various machine learning models, supported by feature extraction methods like Bag of Words (BoW) and TF-IDF, are used to categorize sentiment with measures such as accuracy, precision, recall, and F1 score. Long short term memory (LSTM), k nearest neighbour (KNN) machine classifiers, when combined with BoW data, achieve the highest accuracy. Subject modeling reveals common e-learning challenges, including insufficient network infrastructure, children's difficulties with online learning, and uncertainties about campus reopening. © 2024 IEEE.

关键词： Tweets

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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The Power of PDF-to-Audio Summaries in Modern Learning 4

The Power of PDF-to-Audio Summaries in Modern Learning

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kawde, Dhanshri Mendhe, Sameer Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

PDF -to-audio based summarization technology is designed to enhance information accessibility and efficiency by transforming long written reports into short, audible summaries. AI, NLP, and TTS technologies that have developed recently have made it possible to extract main points without losing context. Yet, current systems have various shortcomings such as inability to process intricate PDF layouts like tables, charts, and annotations, contextual summarization inaccuracies, robotic or unhuman speech synthesis, and limited customization. Furthermore, the absence of multilingual support and easy integration into various platforms hinders wider adoption. These issues are addressed in this paper with a better approach that employs sophisticated NLP methods, context-sensitive summarization algorithms, and adaptive TTS systems. Our goal is to create a strong, user-oriented solution that provides greater summarization accuracy, personalized speech output, and improved accessibility for a variety of users, such as professionals, students, and the disabled. © 2025 IEEE.

关键词： Web Design

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