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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Chandramma Narasimhayya, B.E. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

In modern communication networks, channel sensing and allocation is a crucial part of achieving reliable and efficient network performance. A common approach to this problem is to use a genetic algorithm (GA) to optimize a given objective function. This method can be used to efficiently allocate communication channels among a group of users, and has been widely used in various communication networks. A genetic algorithm is based on the principles of evolutionary biology, and is an optimization technique that uses a population of solutions that evolve to a better solution over time. The algorithm begins by generating a population of solutions, which can be represented by an array of parameters. These parameters are then evaluated using a given objective function, which measures the fitness of each solution. The best solutions are then selected to form the next generation, while the less fit solutions are eliminated. This process is repeated until a given number of generations has been reached, or until the best solution has been found. © 2023 IEEE.

关键词： Genetic algorithms

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Detecting COVID-19 and Lung Diseases Utilizing Parallel CNN Model on Chest X-Rays Enhanced with GradCAM Visualization 2

Detecting COVID-19 and Lung Diseases Utilizing Parallel CNN ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Kibria, Hafsa Binte Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

The emergence of COVID-19 has underscored the urgency of accurate medical diagnosis, particularly in the context of chest X-ray image classification for various lung conditions, including COVID-19, normal cases, viral pneumonia, and lung opacity. In light of the pandemic's impact, there is a pressing need to develop resource-efficient solutions for automated classification, specifically tailored for resource-constrained devices. This article addresses this need by proposing a Lightweight Parallel CNN (Convolutional Neural Network) architecture capable of effectively categorizing the aforementioned multi-class cases. Leveraging a comprehensive dataset of 21165 images, our model achieves a decent accuracy of 93.21% for four-class classification. Notably, the proposed architecture maintains efficiency with fewer than 783k parameters, demonstrating its potential for deployment on devices with limited computational resources. This research contributes to the advancement of accessible and accurate medical diagnostics, catering to the demand for streamlined and reliable solutions amidst challenging circumstances. © 2024 IEEE.

关键词： COVID-19

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Personality Prediction Based on Contextual Feature Embedding SBERT

Personality Prediction Based on Contextual Feature Embedding...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Akber, Md Ali Ferdousi, Tahira Ahmed, Rasel Asfara, Risha Rab, Raqeebir Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665482585

Personality prediction defines an individual's interior self and provides an overview of their behavioral characteristics. Individuals can develop personally and professionally with its aid. Since its inception, the MBTI has become one of the most valuable instruments available due to its widespread application in a variety of fields. Typically, psychologists use questionnaires or conduct interviews with subjects to make predictions. However, because it is only a question-and-answer session, it is prone to error. In this paper, an implicit model is suggested in order to optimize the process using machine learning. The primary objective of this paper is to use sentence transformers to discern the context of user-written social media posts in order to automate the process. In our proposed work, various text pre-processing techniques, such as data cleansing, stopword removal, and data balancing techniques such as random oversampling, are utilized. The context of the text posts is determined using Sentence-BERT (SBERT), a pre-trained model created especially for sentence-level embeddings. Using the Myers-Briggs Type Indicator (MBTI) and a variety of machine learning techniques, such as Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN) and Random Forest (RF) Classifier, it is possible to predict people's personalities based on text. SBERT combined with the Random Forest Classifier performs outstandingly to predict the MBTI personality. © 2023 IEEE.

关键词： Support vector machines

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COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

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computers, Materials & Continua 2022年第2期70卷 2507-2524页

作者： Ahmed F.Ibrahim M.Hassaballah Abdelmgeid A.Ali Yunyoung Nam Ibrahim A.Ibrahim Department of Computer Science Faculty of Computer ScienceNahda UniversityBanisuefEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Faculty of Computers and Information Minia UniversityAlMiniaEgypt Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

关键词： COVID19 COVID data sentiment analysis hierarchical clustering sentiment tree

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Detection and Classification of Grape Leaf Diseases using EfficientNet Model

Detection and Classification of Grape Leaf Diseases using Ef...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Subramanya, S.G. Parkavi, A. Ramaiah Institute of Technology affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350388602

Viticulture or grape cultivation is a significant horticulture method all over the globe. Often, grapevines suffer from many viral, fungal, and bacterial infections that affect production quantity and quality. Farmers worldwide use traditional methods to detect and treat these diseases. However, they are time-consuming, labor intensive, and require significant expertise. The EfficientNetB0 model is implemented for automating the diagnosis of grape leaf diseases. The transfer learning (TL) approach is leveraged to train and fine-tune the EfficientNetB0 model using Kaggle grapevine disease dataset. The leaf image dataset represents four classes - black rot, esca, leaf blight, and healthy samples. The model's performance is evaluated based on metrics like accuracy and loss. The result shows that EfficientNetB0 gave accuracies of 92.45% and 96.73% on training and validation datasets, respectively. The model achieves better results in disease classification while maintaining computational efficiency. © 2024 IEEE.

关键词： Transfer learning

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A Review of Blockchain-Based E-Voting Systems: Comparative Analysis and Findings

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第23期17卷 49-67页

作者： Fatih, Rabia Arezki, Sara Gadi, Taoufiq Faculty of Sciences and Techniques Mathematics Computer Science and Engineering Sciences Laboratory Hassan first University of Settat Settat Morocco

The emergence of blockchain has ushered in a significant transformation in information systems research. Blockchain’s key pillars such as decentralization, immutability, and transparency have paved the path for extensive exploration in various research domains. This particular study is focused on electronic voting, aiming to improve voting procedures by making better use of the benefits offered by blockchain technology. Through a comprehensive review of existing literature, we highlight the potential benefits of blockchain-based electronic voting systems such as transparency, security, and efficiency. However, several challenges, such as scalability, personal data confidentiality, and ensuring robust identity verification, persist. Addressing these issues is necessary to unlock the full potential of blockchain-based electronic voting systems, thereby fostering the development of trustworthy election systems in the future. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Blockchain

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Sand Dune Computing (SDC): A New Framework for Efficient Execution of User Requests to Support Device-Server Model 21

Sand Dune Computing (SDC): A New Framework for Efficient Exe...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Bhoi, Sourav Kumar Department of Computer Science and Engineering Berhampur India

ISBN: (纸本)9798350358230

In the current world of computing, due to tremendous rise of smart devices all over the world it is difficult to get quick service from the limited servers in the heterogeneous environment as some servers are highly loaded with device requests. So, there should be a model or framework to analyze the requests and server traffic in the device-server model with some solution that can execute the device tasks very efficiently. Sand Dune Computing (SDC) is a new computing framework proposed whose main architecture is mainly motivated from a natural process in the environment that is formation of Sand Dunes in areas where the sand grains or smaller loose particles of sands are blown by wind, and accumulated as a temporary hill like structure called as Dune. Here, in the SDC architecture the Sand Grains are mainly considered as the device requests from multiple smart devices to get service from the servers, where server with more traffic is considered as the Sand Dune where the waited device requests are queued. Wind is assumed as the rate of requests from the smart devices. This architecture of SDC will help the current generation technologies or systems such as Internet of Things, cloud computing, edge/fog computing, osmotic computing, ad hoc and sensor networks, distributed computing, AI, etc. to analyze the device requests and server loads based on Sand Area, Dune Area, Dune Size, Dune Frequency, Wind Speed, and Wind Frequency. Also, in this paper some applications of SDC and research challenges are discussed. © 2023 IEEE.

关键词： Sand

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Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine Learning Models 5th

Production Classification in E-Commerce Based on Product Des...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Bist, Yuvraj Gurbaxani, Paramesh Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

The rapid growth of the internet has increased people's reliance on it for expressing opinions on products and stores. Text sentiment analysis is now a key research area. Deep learning methods are commonly used for text classification, but they suffer from issues like information loss and weak context. This paper enhances the existing models to simplify the process, educe training time, and improve overall recall and accuracy in text sentiment classification. With the rapid development of artificial intelligence, traditional manual techniques are fading, and AI algorithms are driving the swift progress of text sentiment classification. Product categorization is critical in e-commerce since it influences search, recommendations, inventory, and consumer experience. NLP approaches and models such as TF-IDF and Word2Vec are effective for categorizing products based on their textual descriptions. This literature study investigates the research evolution, problems, and upcoming trends in using TF-IDF and Word2Vec for product classification in e-commerce. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Adversarial machine learning

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Applying Transformer Models for Automatic Build Errors Classification of Java-Based Open Source Projects 24

Applying Transformer Models for Automatic Build Errors Class...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Lee, Jonathan Li, Mason Hsu, Kuo-Hsun Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Taichung University of Education Taichung Taiwan

ISBN: (纸本)9798400705021

In open-source development, encountering build failures is a common challenge. Addressing these issues requires analyzing the causes of errors and developing solutions for fixing them. In this work, we fine-tuned Google's BERT, a well-known language model excellent in transfer learning, to address build issues in Gradle Java projects. Our strategy utilizes this model to classify error logs and identify fixing solutions. This approach extends our previous work, Gradle ACFix, an automated build error fixing system, to explore the potential of using machine learning to classify error types and identify appropriate fixing strategies for software projects. We gathered a dataset of 11,483 open-source Gradle Java projects from GitHub for this research. The model's evaluation on the error logs of these projects demonstrated a high accuracy rate exceeding 98%. © 2024 IEEE computer Society. All rights reserved.

关键词： Errors

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