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4th Annual International Conference on Data Science, Machine Learning and Blockchain technology, AICDMB 2023

作者： Abdu, Nail Adeeb Ali Basulaim, Khaled Omer Information Technology Department Faculty of Engineering & Computing University of Science and Technology Aden Yemen Information Technology Engineering Department Faculty of Engineering University of Aden

ISBN: (纸本)9781032426853

The availability of time series streaming data has increased dramatically in recent years. Since the last decade, there has been a growing interest in learning from realtime data. While extracting significant information from data streams, online learning faces changes in data distribution. Concept drift refers to hidden data contexts that learning systems are unaware of it. Using previous training instances from the data stream, the classifier categorizes new instances. Hence in this regard, it is obvious to have considerable methods to discover the similarity or diversity status of the present streaming data that compared to the buffered data. This issue is defined and explored in contemporary literature as concept drift. This paper was initiated to identify the constraints of the contemporary methods devised to handle the concept drift in data streams in relation to supervised learning, we also review and categories the concept drift detectors with their key points. Eventually, the article presented observations can provide decision boundary detection at improved levels and discussing open research challenges and possible new research direction. © 2024 The Author(s).

关键词： Machine learning

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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The Integration of Deep Learning Techniques and Big Data Analytics for Improved Breast Cancer Diagnosis and Treatment: A Systematic Review

The Integration of Deep Learning Techniques and Big Data Ana...

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2024 International Conference on information and Communication technology for Development for Africa, ICT4DA 2024

作者： Gebre, Hambisa Mitiku Wegari, Getachew Mamo Jimma University Faculty of Computing and Informatics Department of Information Science Jimma Ethiopia Jimma University Faculty of Computing and Informatics Department of Information Technology Jimma Ethiopia

ISBN: (纸本)9798331527792

Background:- Breast cancer is one of the most common and lethal diseases in the world. Traditional breast cancer diagnostic and prognosis procedures usually involve significant human talent and can be time-consuming and subjective, leading to potential errors and treatment delays. Two recent technical advancements, deep learning, and big data analytics are promising to improve breast cancer diagnosis and therapy. As a result, this systematic review aims to examine specific papers that describe deep learning techniques and big data analytics in the context of breast cancer diagnosis and ***: Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), peer-reviewed articles published in the English language from January 2020 to August 2023 were selected from electronic databases such as PubMed, ACM, Digital Library, and Science Direct, as well as citations and manual searches. This review paper takes into account papers about deep learning algorithms, big data analytics, the efficacy of deep learning and big data analytics, as well as the problems and limitations of merging deep learning methods with big data analytics. Articles that were not original or in English were not ***: - Ten articles were identified for this review. The finding showed that deep-learning techniques play a great role in analyzing vast datasets to identify malignant cells or tumors, aiding radiologists in accurate diagnoses and improving patient outcomes. Big data analytics in breast cancer diagnosis and treatment can improve accuracy, efficiency, and patient-centered care. Deep learning techniques are utilized with big data, enhancing screening test accuracy and guiding diagnostic procedures, especially for image-based scanning that leads to early breast cancer identification, improving patient outcomes, and potentially enhancing diagnostics. Obtaining huge volumes of high-quality data for training deep learning models is a challenge and limita

关键词： big data analytics breast cancer diagnosis and treatment Deep learning algorithms

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Prediction of Heart Disease using an Ensemble Learning Approach

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International Journal of Advanced Computer Science and Applications 2023年第8期14卷 1089-1097页

作者： Alshehri, Ghalia A. Alharbi, Hajar M. Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The ability to predict diseases early is essential for improving healthcare quality and can assist patients in avoiding potentially dangerous health conditions before it is too late. Various machine learning techniques are used in the medical field. Nonetheless, machine learning is critical in determining the future of pharmaceuticals and patients’ health. This is because the various classification techniques provide a high level of accuracy. However, because so much data are being gathered from patients, it becomes harder to find meaningful cardiac disease predictions. A vital research task is to identify these characteristics. Individual classification algorithms in this situation cannot generate flawless models capable of reliably predicting heart disease. As a result, higher performance might be achieved by using ensemble learning approaches (ELA), producing accurate cardiac disease predictions. In the present research work, we utilized an ELA for the early prediction of heart disease, using a new combination including four machine learning algorithms—adaptive boosting, support vector machine, decision tree, and random forest—to increase the accuracy of the prediction results. We used two wrapper methods for feature selection: forward selection and backward elimination. We used the proposed model with three datasets: the StatLog UCI dataset, the Z-Alizadeh Sani dataset, and the Cardiovascular Disease (CVD) dataset. We obtained the highest accuracy when using our proposed model with the Z-Alizadeh Sani dataset, where it was 0.91, while the StatLog UCI dataset was 0.83. The CVD dataset obtained the lowest accuracy, 0.73. © (2023), (Science and information Organization). All Rights Reserved.

关键词： Forecasting

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Opinion mining for stock trend prediction using deep learning

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Multimedia Tools and Applications 2024年 1-24页

作者： Albahli, Saleh Nazir, Tahira Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Faculty of Computing Riphah International University Islamabad Pakistan

Stock market prediction by using Machine Learning (ML) models has been a hot topic of research for more than a decade. Combined with the power of sentiment analysis and ML, social media posts like tweets and financial news can have a big impact on how accurately stock market price predictions are made. Our proposed work makes use of social media posts from Twitter and Google Finance data to predict the price of the stock market using DL techniques. To perform this job, we propose a method that first introduces a concept of fake tweets detection and removal using BERT and then we prepare our dataset using preprocessing techniques. The feature extraction is performed using FastText and VADER, after that the prediction is made through the Deep Belief Network. Two datasets (i.e., Sentiment140 and Twitter real-time data) were used in the experiments and attained 89.12% accuracy along with precision, recall, and F1-Score of 90.15%, 89.12%, and 89.67%. This research not only refines stock market prediction accuracy through a sophisticated blend of sentiment analysis and ML but also introduces novel methodologies in fake tweet detection, feature extraction, and prediction models, charting new territories in the pursuit of precision and reliability. Results reveal that the proposed method performs better in terms of overall accuracy than the most recent methods. We intend to expand the functionality of our approach in the future by including additional well-known social media, such as Facebook and Google News, etc. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

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Computers, Materials & Continua 2023年第1期74卷 1723-1734页

作者： Awny Sayed Maha Mahrous Enas Elgeldawi Computer Science Department Faculty of ScienceMinia UniversityMinia61519Egypt Faculty of Computing and Information Technology Information Technology DepartmentKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationMinia UniversityMinia61519Egypt

Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern.A robust data security system should protect the information assets of any organization against cybercriminal *** Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid *** when it comes to security,it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better *** applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard(AES)algorithm to construct their main security *** this paper,a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast *** new algorithm has been named Split-n-Swap(SnS).Performance analysis of the new modification algorithm has been performed using different measurement *** experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization.A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.

关键词： Twofish advanced encryption standard(AES) cryptography symmetric key block cipher

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VeriFace:Defending against Adversarial Attacks in Face Verification Systems

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Computers, Materials & Continua 2023年第9期76卷 3151-3166页

作者： Awny Sayed Sohair Kinlany Alaa Zaki Ahmed Mahfouz Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Computer Science Department Faculty of ScienceMinia UniversityAl MinyaEgypt Faculty of Computer Studies Arab Open UniversityMuscatOman

Face verification systems are critical in a wide range of applications,such as security systems and biometric ***,these systems are vulnerable to adversarial attacks,which can significantly compromise their accuracy and *** attacks are designed to deceive the face verification system by adding subtle perturbations to the input *** perturbations can be imperceptible to the human eye but can cause the systemtomisclassifyor fail torecognize thepersoninthe *** this issue,weproposeanovel system called VeriFace that comprises two defense mechanisms,adversarial detection,and adversarial *** first mechanism,adversarial detection,is designed to identify whether an input image has been subjected to adversarial *** second mechanism,adversarial removal,is designed to remove these perturbations from the input image to ensure the face verification system can accurately recognize the person in the *** evaluate the effectiveness of the VeriFace system,we conducted experiments on different types of adversarial attacks using the Labelled Faces in the Wild(LFW)*** results show that the VeriFace adversarial detector can accurately identify adversarial imageswith a high detection accuracy of 100%.Additionally,our proposedVeriFace adversarial removalmethod has a significantly lower attack success rate of 6.5%compared to state-of-the-art removalmethods.

关键词： Adversarial attacks face aerification adversarial detection perturbation removal

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Stepwise monogamous pairing genetic algorithm method applied to a multi-depot vehicle routing problem with time windows

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Neural computing and Applications 2025年第16期37卷 9639-9668页

作者： Lim, Ting Yee Ng, Xin Ju Tan, Choo Jun Lim, Chee Peng Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology 77 Lorong Lembah Permai Tiga Penang Tanjong Bungah11200 Malaysia Department of Computing Technologies Swinburne University of Technology John Street HawthornVIC3122 Australia

Evolutionary computation (EC) is a powerful tool for global optimization across various domains, including healthcare logistics. However, the prevalence of local optima in complex problems often hinders the ability of the populations to find optimal solutions, leading to premature convergence. More daunting is that complex optimization problems motivated from real-world challenges continue to pose huge hurdles for EC, with many remaining unresolved. To fill this gap, we propose a new framework named SW-MopGA (Stepwise Monogamous Pairing Genetic Algorithm), which hinges on two innovations. First, a controller for diversity maintenance is introduced into MopGA to generate perturbed opposite solution via opposition-learning when necessary. In doing so, the algorithm is able to maintain population diversity effectively, which alleviates the risk of premature convergence. Second, SW-MopGA proposes a two-step framework: Initially, the population undergoes evolution on a simplified version of the originally complex problem. Solutions derived from the simplified problem serve as building blocks (stepping stones) upon which more intricate blocks are constructed for the final problem—a form of incremental evolution. Numerical analysis on two real-world cases in the logistics healthcare sector demonstrates the effectiveness of SW-MopGA. The problems are formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Overall, the combined effect of the two innovations resulted in up to 18% and 8% savings in terms of total travel time and distance, and vehicles used, respectively on an off-peak day scenario;while approximately 17% and 7% savings for the same criteria on a peak-day scenario, compared to the baseline MopGA. Additionally, SW-MopGA improved the best-known solution of three instances in the MDVRPTW benchmark dataset. Finally, algorithmic insights are provided. In general, our initial study shows encouraging outcomes for incremental evolution and its

关键词： Travel time

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A BERT-Based Approach to Keyword Search for Digital Evidence Analysis 6

A BERT-Based Approach to Keyword Search for Digital Evidence...

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6th International Conference on Advancements in computing, ICAC 2024

作者： Rathnayaka, Gethmi Gonawala, Samesha Dananjana, Pawani Arambawela, Jithmi Senarathne, Amila Siriwardena, Deemantha Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

Existing forensic tools, especially those relying on traditional keyword searches, often fall short in efficiency and accuracy. These tools are vulnerable to human error, bias, and investigator fatigue, which can result in the omission of crucial evidence, thereby undermining the success of investigations. Keyword searching, although a widely used technique across various types of digital investigations, frequently proves inadequate when faced with complexities such as large data volumes, data variety, unstructured data, and context-dependent information of modern data sets. This research presents an innovative AI-powered approach to keyword searching designed to overcome the complexities of traditional methods. By incorporating advanced Bidirectional Encoder Representations from Transformers (BERT), a machine learning (ML) algorithm, and natural language processing (NLP) techniques, the proposed identifies relevant terms and entities within large and complex data sets. This approach accelerates the investigative process and has a final accuracy of approximately 92.5%, reducing the risk of overlooking critical evidence by leveraging techniques like semantic search, contextual understanding, and conceptual matching to identify relevant information even when exact keywords or variations are not used. The adoption of AI-driven keyword searching is expected to transform digital forensic investigations by providing a more reliable, efficient, and comprehensive tool for evidence discovery. © 2024 IEEE.

关键词： Semantics

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