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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Manful, John Terence Hasford, Adelle Yeluripati, Govindha Ramaiah Ashesi University Dept. of Computer Science & Information Systems Berekuso Ghana

ISBN: (纸本)9798350385403

The decentralized nature, self-regulation, and rapid transaction speeds of the cryptocurrency landscape foster both innovation and fraud. As crypto gains attention, fraudulent exchanges posing as legitimate platforms present significant financial risks. Traditional reporting methods, like those of CryptoScamDB, are becoming less effective. This study addresses the need for automated techniques to identify and classify fraudulent exchanges. We found that features related to domain information, website security, and user reviews are crucial for detection. A comparative experiment using machine learning models - random forest, support vector machine, k-nearest neighbour, light gradient boosting machine, and multi-layer perceptron - showed that the light gradient boosting machine (95.9%) and random forest (95.5%) had the highest accuracy. An ensemble model using soft voting achieved a higher accuracy of 96.2%, outperforming its base models. These results demonstrate that machine learning is a viable approach for automating the classification of fraudulent exchanges. © 2024 IEEE.

关键词： Adversarial machine learning

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A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

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computers, Materials & Continua 2023年第1期74卷 1457-1473页

作者： Zahid Ullah Mona Jamjoom Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Alzheimer’s disease(AD)is a chronic and common form of dementia that mainly affects elderly *** disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear,and there is nomedicinal or surgical treatment available yet *** loss of memory and functionality control in multiple degrees according to AD’s progression ***,early diagnosis of AD can hinder its *** imaging tools such as magnetic resonance imaging(MRI),computed tomography(CT)scans,positron emission tomography(PET),*** help in medical diagnosis of ***,computer-aided diagnosis(CAD)such as deep learning applied to brain images obtained with these tools,has been an established strategic methodology that is widely used for clinical assistance in prognosis of *** this study,we proposed an intelligent methodology for building a convolutional neural network(CNN)from scratch to detect AD stages from the brain MRI images dataset and to improve patient *** is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting ***,for better understanding of classifiers and to overcome the model overfitting problem,we applied data augmentation to the minority classes in order to increase the number of MRI images in the *** experiments were conducted using Alzheimer’s MRI dataset consisting of brain MRI scanned *** performance of the proposed model determines detection of the four stages of *** results show high performance of the proposed model in that the model achieved a 99.38%accuracy rate,which is the highest so ***,the proposed model performance in terms of accuracy,precision,sensitivity,specificity,and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

关键词： Dementia MRI images CNN model detection data augmentation decision support system

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Hand Gesture Classification for the Deaf and Mute Using the DenseNet169 Model 9

Hand Gesture Classification for the Deaf and Mute Using the ...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Dabwan, Basel A. Gazzan, Mazen Ismil, Omar A. Farah, Enaam A. Almula, Soad M. Ali, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen College of Computer Science and Information Systems Najran University Department of Information System Saudi Arabia Najran University Department of Information System Saudi Arabia Najran University Department of Computer Science Saudi Arabia College of Computer Science and Information Systems Najran University Department of Computer Science Saudi Arabia Collage of Computer Science and Information Systems Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350377972

Sign language is a crucial means of communication for people who are deaf or hard of hearing, relying on specific signs, hand shapes, gestures, and facial expressions to express ideas and information. Since many deaf individuals face challenges with verbal communication, reading, and writing, developing a Sign Language Recognition (SLR) system is crucial for enhancing their ability to interact with hearing individuals. This paper presents a solution using the DenseNet169 model to recognize American Sign Language (ASL) gestures. We utilized a dataset representing the ASL alphabet, consisting of 24 classes (excluding the letters J and Z, which involve movement), with a The dataset contains 27,455 samples for training and 7,172 samples for testing. It was divided such that 80% of the data was used for training and 20% for testing. The proposed model has achieved impressive results, with a validation accuracy of 98%, highlighting its potential to significantly enhance communication for the hearing-impaired community. This work contributes to the advancement of human-computer interaction by providing an effective tool for recognizing sign language gestures and facilitating communication. © 2024 IEEE.

关键词： Human computer interaction

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Leveraging Artificial Intelligence to Passively Record Sales in Informal Sector Convenience Stores 9

Leveraging Artificial Intelligence to Passively Record Sales...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Yakung, Chudah Annor-Adjaye, Leanne Owusu, Dennis Asamoah Ashesi University Dept.of Computer Science & Information Systems Berekuso Ghana

ISBN: (纸本)9798350385403

Informal sector convenience stores play a vital role in the economy of African countries. They provide employment and contribute significantly to the economy. Effective sales recording is crucial for these shops as it enables them to obtain financial loans, garner insights into customer preferences, and facilitate inventory management. However, these shops struggle to record sales. Current solutions to address this challenge have their own limitations. They require active participation from shop attendants, which may introduce errors. Given the increasing relevance of Artificial Intelligence (AI) in today's world, this paper explores the potential of AI to passively record sales in these informal sector convenience stores, hence contributing to the body of knowledge on improving retail record-keeping operations in Africa. It introduces a novel intelligent system designed to automate sales recording in these informal sector convenience shops. The results indicate that AI can be effectively utilised to passively record sales in informal sector convenience stores. © 2024 IEEE.

关键词： Sales

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An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining

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computer systems Science & Engineering 2022年第8期42卷 779-794页

作者： Surbhi Bhatia Mohammed AlOjail Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

Opinion summarization recapitulates the opinions about a common topic *** primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the *** need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text *** paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory(RNN-LSTM)deep learning model for short and exact synopsis using seq2seq *** presents a paradigm shift with regard to the way extractive summaries are *** algorithms for word extraction using assertions are *** semantic framework is well-grounded in this research facilitating the correct decision making process after reviewing huge amount of online reviews,considering all its important features into *** advantages of the proposed solution provides greater computational efficiency,better inferences from social media,data understanding,robustness and handling sparse *** on the different datasets also outperforms the previous researches and the accuracy is claimed to achieve more than the baselines,showing the efficiency and the novelty in the research *** comparisons are done by calculating accuracy with different baselines using Rouge tool.

关键词： Sentiment analysis data mining text summarization opinion mining principal component analysis

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Predicting Drug-Drug Interactions with Machine-Learning and ATC-SMILES Combined Representation 6

Predicting Drug-Drug Interactions with Machine-Learning and ...

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6th International Conference on Intelligent Computing in Data Sciences, ICDS 2024

作者： Radwan, Yasmin Atef Gouda, Karam Abdelghany Abdelbaky, Ibrahim Zaghloul Arafa, Mona Mohamed Higher Institute of Computer Science & Information Technology EI-Shorouk Academy Information Systems Department Egypt Benha University Faculty of Computers and Artificial Intelligence Information Systems Department Egypt

ISBN: (纸本)9798350351200

Polypharmacy is a potential strategy for managing such intricate disorders, encompassing conditions like cancer, diabetes, and age-related issues in older individuals. Nonetheless, when a medication is combined with one or more drugs that either enhance, diminish, or counteract its intended effects, it can lead to undesired adverse reactions. In severe cases, these interactions can cause serious morbidity and increased mortality rates globally. In this study, we collected a Drug-Drug interaction dataset from the DrugBank database. Various chemical features were then extracted from the Simplified Molecular-Input Line-Entry System of interacting drug pairs. Our emphasis was on representing the Molecular Access System fingerprints of these drug pairs. Molecular Access System fingerprints signify the presence or absence of specific substructures in the molecule and were generated using the RDKit Open-Source Cheminformatics Software. Furthermore, we incorporated Anatomical Therapeutic Chemical classifications into our analysis. Finally, we employed various machine learning algorithms, using K-Nearest Neighbor, Random Forest, Logistic Regression, Support Vector Machine, and XGBoost for learning the extracted features and predict large-scale Drug-Drug interactions among various drug pairs. Among these models, the XGBoost model exhibited superior performance across most measurement metrics. © 2024 IEEE.

关键词： Nearest neighbor search

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Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

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computers, Materials & Continua 2023年第6期75卷 4805-4820页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Sciences College of Computing and Information SystemsUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile *** difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing *** the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same ***,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing *** this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)*** presented COAER-UAVC technique establishes effective routes for communication between the *** is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from ***,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC *** experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

关键词： Artificial intelligence unmanned aerial vehicle data communication routing protocol energy efficiency

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Experience in Teaching and Engaging computer Science and computer information systems Students in Active Learning

Experience in Teaching and Engaging Computer Science and Com...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Weizheng Gao Olumide Malomo Somasheker Akkaladevi Ephrem Eyob Adeyemi A. Adekoya Shanzhen Gao Julian D. Allagan Jianning Su Department of Mathematics and Computer Science College of Natural Sciences and Mathematics West Virginia State University Institute WV USA Department of Computer Information Systems Reginald F Lewis College of Business Virginia State University Petersburg VA USA Department of Mathematics Computer Science and Engineering Technology Elizabeth City State University Elizabeth City NC USA Department of Mathematics and Sciences Midway University Midway KY

Active learning refers to a broad range of teaching and pedagogical strategies that engage students as “partners-in-progress” participants in their learning during class time with their instructors. We discuss some real-life experiences related to students' engagement in an early introductory course to computer science. In this paper, commonly known best teaching practices such as Active Learning, Problem-Based Learning, and Integrated Learning are implemented and proven to be effective given students' survey responses.

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Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

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Intelligent Automation & Soft Computing 2023年第3期35卷 3129-3140页

作者： Hanan Abdullah Mengash Alaaeldin M.Hafez Hanan A.Hosni Mahmoud Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Encephalitis is a brain inﬂammation *** can yield to seizures,motor disability,or some loss of vision or ***,encepha-litis can be a life-threatening and proper diagnosis in an early stage is very ***,in this paper,we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data(EEG).Also,we propose a Density-Based Clustering model to classify the distinctive waves of *** clustering models usually employ a computed single centroid virtual point to deﬁne the cluster conﬁguration,but this single point does not contain adequate *** precisely extract accurate inner structural data,a multiple centroids approach is employed and deﬁned in this paper,which deﬁnes the cluster conﬁguration by allocating weights to each state in the *** multiple EEG view fuzzy learning approach incorporates data from every sin-gle view to enhance the model's clustering *** a fuzzy Density-Based Clustering model with multiple centroids(FDBC)is *** model employs multiple real state centroids to deﬁne clusters using Partitioning Around Centroids *** Experimental results validate the medical importance of the proposed clustering model.

关键词： Density clustering clustering structural data fuzzy set

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Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

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computers, Materials & Continua 2023年第5期75卷 3743-3759页

作者： Emad Alsuwat Suhare Solaiman Hatim Alsuwat Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)*** to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model *** to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and *** paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the *** goal of this architecture combination is to accurately represent data and create an effective cyber security prediction *** intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware *** data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)*** on the classification output,the decision for the detection of attackers and authorized users will be *** experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.

关键词： Concept drift machine learning DDOS cyber security SAW_WDA MLPGDT

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