Applied Data Science and Analysis is a respected journal dedicated to real-world applications of data science. It provides a platform for experts to share innovative ideas and methodologies. Focused on bridging theory and practice, it showcases cutting-edge research and case studies in data analysis, machine learning, and more. Welcoming diverse contributions from fields like business, healthcare, and social sciences, the journal fosters collaboration among data professionals, aiming to advance the impact of data science in practical settings
A Quantum Resilient Security System for Smart Power Grid Data: Combining Kyber, FALCON, and Zero-Knowledge Proofs Against Quantum Threats
The rapid progress of quantum computing poses significant challenges to traditional cryptographic mechanisms, necessitating the adoption of post-quantum cryptography (PQC) solutions. This paper proposes a Quantum-Enhanced Security for Smart Meters (QESM) system to protect power plant data in smart cities, integrating Kyber for secure key exchange, FALCON (Fast-Fourier Transform over Lattice-based Cryptography) for quantum-resistant digital signatures, and ZKP (Zero-Knowledge Proof) for effective verification without revealing sensitive data to secure power plant data against quantum attacks. To evaluate the security of the proposed system, we analyze its resistance to various quantum threats, including Shor’s algorithm, Grover’s algorithm, quantum key analysis, quantum reversal encryption, quantum amplification, quantum switching, and quantum collision attacks. In the current study, accurate measures were used and the average was approximately 7.065 (bits/byte) for randomness, the average execution time was 6.202 milliseconds, the average memory consumption was approximately 4.343 KB, 6.4 Completeness was equal to 1 and unforgeability was 100%. As for the average throughput, it was approximately 485,605 operations per second. That shows the QESM system provides strong security and efficiency, making it a viable solution for protecting the electricity infrastructure in smart cities in the quantum era.
Copyright (c) 2025 Mishall Al-Zubaidie, Tuqa Ghani Tregi https://creativecommons.org/licenses/by/4.0
Data Classification and Emerging Encryption Technologies in Big Data and Cloud Computing : A Systematic Review
This article provides a comprehensive review of recent advancements in data classification methods within the context of big data and cloud computing. As organizations increasingly rely on massive volumes of digital information, robust classification techniques have become essential, particularly for handling sensitive or confidential data. The study explores key approaches, including automated document classification and encryption-based strategies, each addressing distinct challenges related to security and efficiency. Emphasis is placed on how these methods safeguard data confidentiality, integrity, and availability critical factors in mitigating unauthorized access and cyber threats. The review also identifies pressing research gaps, such as the need for more scalable, efficient, and user-friendly classification systems that can adapt to the evolving nature of big data. The objective is to provide an in-depth overview of current practices, highlight persistent challenges, and outline promising directions for future research in this crucial field.
Copyright (c) 2025 Karthik Kumar Vaigandla https://creativecommons.org/licenses/by/4.0
Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and identifying variants. A patient's HIV strain can be classified as susceptible or resistant to 17 different treatments. The FGD-MCNN transforms DNA genotype and HIV data into mathematical metrics, providing valuable insights into treatment-resistant HIV strains through pooling analysis. With remarkable accuracy, the FGD-MCNN deep learning system predicts HIV medication resistance using behavioral and genome-wide data from the HIV database. DNA patterns can be classified as resistant or susceptible by 17 antiretroviral drugs, providing valuable information for treatment planning and medical judgment. The model's parameter values illustrate the connections between neurons and the complex webs observed in the data have been examined. This study improves treatment effectiveness and expands the knowledge of HIV/AIDS.
Fine-Tuned Gradient Descent Fused Multi-Kernel Convolutional Neural Network (FGD-MCNN)
Copyright (c) 2025 Asma Khazaal Abdulsahib, Geehan Sabah Hassan, Faris M. Alwan, Israa Ibraheem Al_Barazanchi https://creativecommons.org/licenses/by/4.0
The evolving landscape of Cybercrime in Nepal: A multi-Year Analysis of Platform Specific Trends and Victim Demographics (2077-2082 B.S. / 2020-2025 A.D.)
The study analyzes official data provided by Nepal Police Headquarters Cyber Bureau for the fiscal year 2020/2021 A.D. to 2024/2025 A.D., offering a multi-year analysis of cybercrime trends by platform and victim demographics. It examines 53,474 cybercrime reported cases across 15 digital platforms, identifying dominant vectors of cybercriminal activity, and analyzes demographic victimization patterns. Results reveal dramatic reported cybercrime applications from 3,906 in 2020/2021 to 16,139 in 2024/2025., peaking at 19,730 in 2023/2024. Facebook/Messenger is the primary medium for incidents cumulatively accounted for 72.73% of all cases reported. TikTok emerged as a critical threat vector, demonstrating a 3092.86% growth in associated criminal activities. Statistical methods including chi-square tests, and Herfindahl-Hirschman Index were applied to assess platform concentration and trend evolution. Gender-wise analysis show men accounting for 47.02% and women for 45.69%, with women initially reporting more incidents in earlier fiscal years. The study correlates these trends with prevalent cybercrime typologies- photo mutilation, revenge porn, ransomware attacks, defamation and impersonation, hacking and unauthorized access, and online fraud. These findings offer policymakers, cybersecurity professionals, individuals, and researchers’ empirical insights into Nepal’s evolving cybercrime landscape and support the development of targeted prevention strategies and public awareness campaigns in a digitally evolving nation like Nepal.
Copyright (c) 2025 Hemant Dhital https://creativecommons.org/licenses/by/4.0
Lexicon annotation in sentiment analysis for dialectal Arabic: Consensus Expert Standardized Criteria
Sentiment Analysis (SA) in Natural Language Processing (NLP) involves analyzing perceptions, attitudes, and emotions from text. It is crucial for decision-making and consumer insights. Recent studies focus on developing Lexicons for SA research. Understanding the construction and evaluation of existing lexicons is key to advancing development efforts. Evaluation and benchmarking of lexicons are vital for identifying the most suitable ones and establishing best practices. Factors like effectiveness and importance must be considered when building or selecting lexicons. This research outlines three key phases: Determining Lexicons, Identifying Evaluation Criteria, and Engaging Experts. The study aims to enhance understanding of lexicon development processes and improve future guidelines. Efforts in lexicon development can benefit from a structured approach that considers various criteria for evaluation. The research emphasizes the importance of expert input in refining lexicons for optimal performance. Evaluating lexical criteria helps in identifying gaps and areas for improvement in sentiment analysis tools. Benchmarking different lexicons aids in selecting the most appropriate ones for specific applications or domains. Establishing best practices in lexicon development involves thorough evaluation against predefined criteria to ensure quality and reliability. Expert opinions play a crucial role in validating the significance of developed lexicons for sentiment analysis tasks. The research methodology involves systematic identification of lexicons relevant criteria, and experts to inform best practices in the field of sentiment analysis. By focusing on these three key phases, this study aims to contribute valuable insights into enhancing sentiment analysis through improved lexicon development processes.
Copyright (c) 2024 Sameh M. Sherif , A.H. Alamoodi https://creativecommons.org/licenses/by/4.0
Emerging Trends in Applying Artificial Intelligence to Monkeypox Disease: A Bibliometric Analysis
Monkeypox is a rather rare viral infectious disease that initially did not receive much attention but has recently become a subject of concern from the point of view of public health. Artificial intelligence (AI) techniques are considered beneficial when it comes to diagnosis and identification of Monkeypox through the medical big data, including medical imaging and other details from patients’ information systems. Therefore, this work performs a bibliometric analysis to incorporate the fields of AI and bibliometrics to discuss trends and future research opportunities in Monkeypox. A search over various databases was performed and the title and abstracts of the articles were reviewed, resulting in a total of 251 articles. After eliminating duplicates and irrelevant papers, 108 articles were found to be suitable for the study. In reviewing these studies, attention was given on who contributed on the topics or fields, what new topics appeared over time, and what papers were most notable. The main added value of this work is to outline to the reader the process of how to conduct a correct comprehensive bibliometric analysis by examining a real case study related to Monkeypox disease. As a result, the study shows that AI has a great potential to improve diagnostics, treatment, and public health recommendations connected with Monkeypox. Possibly, the application of AI to Monkeypox study can enhance the public health responses and outcomes since it can hasten the identification of effective interventions.
Copyright (c) 2024 Yahya Layth Khaleel , Mustafa Abdulfattah Habeeb , Mustafa Abdulfattah Habeeb , Rabab Benotsmane https://creativecommons.org/licenses/by/4.0
Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches
There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter the decision region. These attacks are committed to presenting different data to machine learning models in a way that the model would be wrong in its classification or prediction. The field of study is still relatively young and has to develop strong bodies of scientific research that would eliminate the gaps in the current knowledge. This paper provides the literature review of adversarial attacks and defenses based on the highly cited articles and conference published in the Scopus database. Through the classification and assessment of 128 systematic articles: 80 original papers and 48 review papers till May 15, 2024, this study categorizes and reviews the literature from different domains, such as Graph Neural Networks, Deep Learning Models for IoT Systems, and others. The review posits findings on identified metrics, citation analysis, and contributions from these studies while suggesting the area’s further research and development for adversarial robustness’ and protection mechanisms. The identified objective of this work is to present the basic background of adversarial attacks and defenses, and the need for maintaining the adaptability of machine learning platforms. In this context, the objective is to contribute to building efficient and sustainable protection mechanisms for AI applications in various industries
Copyright (c) 2024 Yahya Layth Khaleel , Mustafa Abdulfattah Habeeb , Hussein Alnabulsi https://creativecommons.org/licenses/by/4.0
Transforming Amazon's Operations: Leveraging Oracle Cloud-Based ERP with Advanced Analytics for Data-Driven Success
Background: This research paper discusses a detailed exploration of Amazon's adoption of Oracle ERP Cloud, focusing on the strategic benefits of the implementation and the challenges and wider implications of implementing cloud-based ERP solutions within one of the world's largest and most complex enterprises. Further, it is detailed how, through a strict selection process, Amazon was led to settle for Oracle ERP Cloud from several leading ERP systems in the market. It also brings forth the criteria and evaluations at hand that guided this decision-making. Method: This technique focuses on the phased rollout strategy, showing how Amazon brought the ERP system incrementally across departments, beginning with finance and procurement. It underlines the important role played by cross-functional teamwork, depicting efforts between finance, supply chain, HR, and IT teams to smooth implementation. Results: The study shows how deep technologies such as AI, machine learning, the Internet of Things, and blockchain are integrated into the ERP system. These go a long way to increase the decision-making ability and better operation of security, with improved transparency in Amazon; they provide it with real-time analytics, predictive insights, and improved transparency. Conclusion: Implementing Oracle ERP Cloud at Amazon sheds light on how scalable and cost-efficient cloud-based ERP solutions are. The availability of real-time data access and advanced analytics has spurred data-driven decision-making, but issues such as data migration and security require careful consideration in the planning process. This work provides valuable insights for enterprises seeking to implement similar ERP systems.
Copyright (c) 2024 Tahsien Al-Quraishi, Osama A. Mahdi , Ali Abusalem , Chee Keong NG , Amoakoh Gyasi , Omar Al-Boridi , Naseer Al-Quraishi https://creativecommons.org/licenses/by/4.0
Is LiFi Technology Ready for Manufacturing and Adoption? An End-user questionnaire-based study
Because of the exponential development of emerging technologies and the increase of devices that use the internet, the wireless fidelity (WiFi) spectrum has been saturated, therefore, the light fidelity (LiFi) has been under development for wireless communication including internet access. LiFi network systems can provide high speed data rates with high security. However, LiFi is still under development and research, and is not yet popular for end-users to be used in homes, companies, and other industries. Therefore, for the first time, this study investigates the adoption probability of LiFi technology by the end-users to anticipate the success rate when launching ready-to-use LiFi devices for end-users by the manufacturer companies. A well-designed questionnaire is used in this study for data collection. A total of 100 participants from around the world have been chosen to fill-up the questionnaire forms including three phases: basic information, preferences, and usage, and LiFi and Pricing. The findings of this study show a high and positive probability for adoption rate of LiFi technology. However, the pricing aspect has a critical impact on the acceptance of using LiFi systems by the end-users.
Copyright (c) 2024 Sallar Salam Murad, Rozin Badeel ; Rehem A. Ahmed https://creativecommons.org/licenses/by/4.0
Optimal Time Window Selection in the Wavelet Signal Domain for Brain–Computer Interfaces in Wheelchair Steering Control
Background and objective: Principally, the procedure of pattern recognition in terms of segmentation plays a significant role in a BCI-based wheelchair control system for avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe situations. Arguably, each subject might have different motor-imagery signal powers at different times in the trial because he or she could start (or end) performing the motor-imagery task at slightly different time intervals due to differences in the complexities his or her brain. Therefore, the primary goal of this research is to develop a generic pattern recognition model (GPRM)-based EEG-MI brain-computer interface for wheelchair steering control. Additionally, having a simplified and well generalized pattern recognition model is essential for EEG-MI based BCI applications. Methods: Initially, bandpass filtering and segmentation using multiple time windows were used for denoising the EEG-MI signal and finding the best duration that contains the MI feature components. Then, feature extraction was performed using five statistical features, namely the minimum, maximum, mean, median, and standard deviation, were used for extracting the MI feature components from the wavelet coefficient. Then, seven machine learning methods were adopted and evaluated to find the best classifiers. Results: The results of the study showed that, the best durations in the time-frequency domain were in the range of (4-7 s). Interestingly, the GPRM model based on the LR classifier was highly accurate, and achieved an impressive classification accuracy of 85.7%.
Copyright (c) 2024 Z.T. Al-Qaysi , M. S Suzani , Nazre bin Abdul Rashid , Rasha A. Aljanabi, Reem D. Ismail , M.A. Ahmed , Wan Aliaa Wan Sulaiman , Harish Kumar https://creativecommons.org/licenses/by/4.0
Advanced Ensemble Classifier Techniques for Predicting Tumor Viability in Osteosarcoma Histological Slide Images
Background: Osteosarcoma is considered as the primary malignant tumor of the bone, emanating from primitive mesenchymal cells that form osteoid or immature bone. Accurate diagnosis and classification play a key role in management planning to achieve improved patient outcomes. Machine learning techniques may be used to augment and surpass existing conventional methods towards an analysis of medical data. Methods: In the present study, the combination of feature selection techniques and classification methods was used in the development of predictive models of osteosarcoma cases. The techniques include L1 Regularization (Lasso), Recursive Feature Elimination (RFE), SelectKBest, Tree-based Feature Importance, while the following classification methods were applied: Voting Classifier, Decision Tree, Naive Bayes, Multi-Layer Perceptron, Random Forest, Logistic Regression, AdaBoost, and Gradient Boosting. Some model assessment was done by combining metrics such as accuracy, precision, recall, F1 score, AUC, and V score. Results: The combination of the Tree-Based Feature Importance for feature selection and Voting Classifier with Decision Tree Classifier proved to be giving a higher performance compared to all other combinations, where such combinations helped in correct classification of positive instances and wonderful minimization of false positives. Other combinations also gave significant performances but slightly less effective, for example, L1 Regularization with the Voting Classifier, RFE with the Voting Classifier. Conclusion: This work presents strong evidence that advanced machine learning with ensemble classifiers and robust feature selection can result in overall improvement of the diagnostic accuracy and robustness for the classification of osteosarcoma. Research on class imbalance and computational efficiency will be its future research priority.
Copyright (c) 2024 Tahsien Al-Quraishi , Chee Keong NG , Osama A. Mahdi , Amoakoh Gyasi, Naseer Al-Quraishi https://creativecommons.org/licenses/by/4.0
An Innovative Method of Malicious Code Injection Attacks on Websites
This paper provides a model to identify website vulnerability to Code Injection Attacks (CIAs). The proposed model identifies vulnerabilities to CIA of various websites, to check vulnerable to CIAs. The lack of existing models in providing checking against code injection has motivated this paper to present a new and enhanced model against web code injection attacks that uses SQL injections and Cross-Site Script (XSS) injections. This paper previews a self-checking protection model which enables web administrators to know whether their current protection program is adequate, or whether a website needs stronger protection against CIAs. The Automated Injection’s model is to check vulnerable to cod injection. The checking methodology consists of many intrusion methods that the attacker may use to launch code injection attacks. Methodology can give a high precision of CIA vulnerability checking for a website compared with other approaches (the minimum accuracy different between proposed approach and other approaches is 3.15%). CIAs can be a serious problem for vulnerable websites including stealing, deleting, or altering important data. Extensive experiments are conducted and compared with existing research [e.g. 1, 5, and 9] to study the effectiveness of the proposed model that can check whether a website is vulnerable to CIAs. The performance of the suggested approach has been tested on SQL injections and XSS injections. The studies showed that the detection rate of our model is 95.27%, and the false positive rate is 5.55%.
Copyright (c) 2024 Hussein Alnabulsi , Rafiqul Islam , Izzat Alsmadi , Savitri Bevinakoppa https://creativecommons.org/licenses/by/4.0
Big Data Predictive Analytics for Personalized Medicine: Perspectives and Challenges
The integration of predictive analytics into personalized medicine has become a promising approach for improving patient outcomes and treatment efficacy. This paper provides a review of the field, examining the tools, methodologies, and challenges associated with this advanced statistical methodology. Predictive analytics leverages machine learning algorithms to analyze vast datasets, including Electronic Health Records (EHRs), genomic data, medical imaging, and real-time data from wearable devices. The review explores key tools such as the Hadoop Distributed File System (HDFS), Apache Spark, and Apache Hive, which facilitate scalable storage, efficient data processing, and comprehensive data analysis. Key challenges identified include managing the immense volume of healthcare data, ensuring data quality and integration, and addressing privacy and security concerns. The paper also highlights the difficulties in achieving real-time data processing and integrating predictive insights into clinical practice. Effective data governance and ethical considerations are critical to maintaining trust and transparency. The strategic use of big data tools, combined with investment in skill development and interdisciplinary collaboration, is essential for harnessing the full potential of predictive analytics in personalized medicine. By overcoming these challenges, healthcare providers can enhance patient care, optimize resource management, and drive medical discoveries, ultimately revolutionizing healthcare delivery on a global scale.
Copyright (c) 2024 Tahsien Al-Quraishi, Naseer Al-Quraishi, Hussein AlNabulsi, Hussein AL-Qarishey, Ahmed Hussein Ali https://creativecommons.org/licenses/by/4.0
Semantic Image Retrieval Analysis Based on Deep Learning and Singular Value Decomposition
The exponential growth in the total quantity of digital images has necessitated the development of systems that are capable of retrieving these images. Content-based image retrieval is a technique used to get images from a database. The user provides a query image, and the system retrieves those photos from the database that are most similar to the query image. The image retrieval problem pertains to the task of locating digital photographs inside extensive datasets. Image retrieval researchers are transitioning from the use of keywords to the utilization of low-level characteristics and semantic features. The push for semantic features arises from the issue of subjective and time-consuming keywords, as well as the limitation of low-level characteristics in capturing high-level concepts that users have in mind. The main goal of this study is to examine how convolutional neural networks can be used to acquire advanced visual features. These high-level feature descriptors have the potential to be the most effective compared to the handcrafted feature descriptors in terms of image representation, which would result in improved image retrieval performance. The (CBIR-VGGSVD) model is an ideal solution for content-based image retrieval that is based on the VGG-16 algorithm and uses the Singular Value Decomposition (SVD) technique. The suggested model incorporates the VGG-16 model for the purpose of extracting features from both the query images and the images kept in the database. Afterwards, the dimensionality of the features retrieved from the VGG-16 model is reduced using SVD. Then, we compare the query photographs to the dataset images using the cosine metric to see how similar they are. When all is said and done, images that share a high degree of similarity will be successfully extracted from the dataset. A validation of the retrieval performance of the CBIR-VGGSVD model is performed using the Corel-1K dataset. When the VGG-16 standard model is the sole one used, the implementation will produce an average precision of 0.864. On the other hand, when the CBIR-VGGSVD model is utilized, this average precision is revealed to be (0.948). The findings of the retrieval ensured that the CBIR-VGGSVD model provided an improvement in performance on the test pictures that were utilized, surpassing the performance of the most recent approaches.
Copyright (c) 2024 M.H. Hadid, Z.T. Al-Qaysi , Qasim Mohammed Hussein, Rasha A. Aljanabi, Israa Rafaa Abdulqader, M. S Suzani, WL Shir https://creativecommons.org/licenses/by/4.0
Deep Transfer Learning Model for EEG Biometric Decoding
In automated systems, biometric systems can be used for efficient and unique identification and authentication of individuals without requiring users to carry or remember any physical tokens or passwords. Biometric systems are a rapidly developing and promising technology domain. in contrasting with conventional methods like password IDs. Biometrics refer to biological measures or physical traits that can be employed to identify and authenticate individuals. The motivation to employ brain activity as a biometric identifier in automatic identification systems has increased substantially in recent years. with a specific focus on data obtained through electroencephalography (EEG). Numerous investigations have revealed the existence of discriminative characteristics in brain signals captured during different types of cognitive tasks. However, because of their high dimensional and nonstationary properties, EEG signals are inherently complex, which means that both feature extraction and classification methods must take this into consideration. In this study, a hybridization method that combined a classical classifier with a pre-trained convolutional neural network (CNN) and the short-time Fourier transform (STFT) spectrum was employed. For tasks such as subject identification and lock and unlock classification, we employed a hybrid model in mobile biometric authentication to decode two-class motor imagery (MI) signals. This was accomplished by building nine distinct hybrid models using nine potential classifiers, primarily classification algorithms, from which the best one was finally selected. The experimental portion of this study involved, in practice, six experiments. For biometric authentication tasks, the first experiment tries to create a hybrid model. In order to accomplish this, nine hybrid models were constructed using nine potential classifiers, which are largely classification methods. Comparing the RF-VGG19 model to other models, it is evident that the former performed better. As a result, it was chosen as the method for mobile biometric authentication. The performance RF-VGG19 model is validated using the second experiment. The third experiment attempts for verifying the RF-VGG19 model's performance. The fourth experiment performs the lock and unlock classification process with an average accuracy of 91.0% using the RF-VGG19 model. The fifth experiment was performed to verify the accuracy and effectiveness of the RF-VGG19 model in performing the lock and unlock task. The mean accuracy achieved was 94.40%. Validating the RF-VGG19 model for the lock and unlock task using a different dataset (unseen data) was the goal of the sixth experiment, which achieved an accuracy of 92.8%. This indicates the hybrid model assesses the left and right hands' ability to decode the MI signal. Consequently, The RF-VGG19 model can aid the BCI-MI community by simplifying the implementation of the mobile biometric authentication requirement, specifically in subject identification and lock and unlock classification.
Copyright (c) 2024 Rasha A. Aljanabi, Z.T. Al-Qaysi , M. S Suzani https://creativecommons.org/licenses/by/4.0
Harnessing the Tide of Innovation: The Dual Faces of Generative AI in Applied Sciences; Letter to Editor
Advancements in Artificial Intelligence (AI) and emerging generative capabilities added paradoxical aspects. One aspect is its positive impact and limitless power it brings to users. On the other hand, concerns about the misuse of this powerful tool have consistently increased [1]. AI advancements affect all domains and sectors as they evolve in their applicable nature in the applied sciences. The more powerful AI the more influence it has on the model workflow within the specific domain and its applied field [2]. This dual nature of generative AI ignited a wide discussion on implementation and produced a debate according to the latest employed tools and technologies by scientists and researchers.
Copyright (c) 2024 A.S. Albahri, Idrees A. Zahid , Mohanad G. Yaseen, Mohammad Aljanabi, Ahmed Hussein Ali, Akhmed Kaleel https://creativecommons.org/licenses/by/4.0
Does Lack of Knowledge and Hardship of Information Access Signify Powerful AI? A Large Language Model Perspective
Large Language Models (LLMs) are evolving and expanding enormously. With the consistent improvement of LLMs, more complex and sophisticated tasks will be tackled. Handling various tasks and fulfilling different queries will be more precise. Emerging LLMs in the field of Artificial Intelligence (AI) impact online digital content. An association between digital corpus scarcity and the improvement of LLMs is drawn. The impact it will bring to the field of LLMs is discussed. More powerful LLMs are insights to be there. Specifically, increase in Reinforcement Learning from Human Feedback (RLHF) LLMs release. More precise RLHF LLMs will endure development and alternative releases.
Copyright (c) 2023 Idrees A. Zahid, Shahad Sabbar Joudar https://creativecommons.org/licenses/by/4.0
A bibliometric analysis of research on multiple criteria decision making with emphasis on Energy Sector between (2019-2023)
In the present study, a bibliometric analysis of research works that have been conducted over the last five years in connection to Multiple Criteria Decision making (MCDM) and its application in the energy sector is presented. In the beginning, a statistical study of influential publications, journals, countries/territories, and authors was carried out. In the following step, an analysis was performed based on four distinct time periods to determine the evolving patterns of authors' cooperation structure and study themes. According to the findings, there has been a rise in the quality of collaboration between writers, as well as an increase in the number of publications and authors who have contributed to the study on MCDM during the last five years. Researchers should be able to successfully conduct investigations in linked domains with the assistance of the complete and scientific analysis of MCDM. It also concludes that there are more opportunities in the future in the field of energy applications with MCDM, and this can be encouraging for researchers from both fields, as well as those from the industrial and economic fields, to consider MCDM in their utilization of energy alternatives and to make decisions that are informed by such findings.
Copyright (c) 2023 Dianese David, Abdullah Alamoodi https://creativecommons.org/licenses/by/4.0
Application of Sequential Analysis on Runtime Behavior for Ransomware Classification
The unprecedented development and massive proliferation of Internet technology, computing /storage capability and emerging business model, like cloud and IoT, brings not only incredible changes to human lifestyle but also numerous, complex and continuing cyber security threats, one noticeable example among them is malware. Static analysis has been popular and widely used in many anti-virus engine. However, static analysis can be avoided using techniques such as packing, polymorphism, and metamorphism. In this paper, I propose a novel method focuses on the feature extraction, which exploits the inherent encryption behaviour of ransomwares. Specifically, runtime malicious sequential analysis is adopted to establish the desired feature set, which further facilitate the identification of the inherent encryption function. With the proposed method, an accuracy level of 96% was achieved
Copyright (c) 2023 Chee Keong NG, Tahsien Al-Quraishi, Tony De Souza-Daw https://creativecommons.org/licenses/by/4.0
Challenges in AutoML and Declarative Studies Using Systematic Literature Review
Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.
Copyright (c) 2023 Eman Thabet Khalid, Abdulla J. Y. Aldarwish, Ali A.Yassin https://creativecommons.org/licenses/by/4.0
Smart Real-Time IoT mHealth-based Conceptual Framework for Healthcare Services Provision during Network Failures
A series of healthcare problems related to frequent failures in telemedicine architecture, particularly in multi-sensors (Tier 1), medical center servers (Tier 3), and potential failures in network integration between these system components, needed to be addressed. The objective of this research was to present a novel smart real-time IoT mHealth framework within the context of IoT that could select an appropriate hospital during the aforementioned failures. The research methodology involved a new local multi-sensor fusion triage algorithm called the three-level localization triage (3LLT). This aimed to exclude the control process of patient triage and sensor fusion from the medical center, while also alarming failures related to medical body sensors. Additionally, the proposed framework was implemented using the multi-criteria decision-making (MCDM) method, connecting mHealth directly with distributed hospital servers. The distribution of hospitals was determined using the AHP (Analytic Hierarchy Process) based on the crossover between ‘healthcare services/time of arrival of the patient at the hospital’ and ‘hospitals list’ to estimate small power consumption. Validation processes were conducted for the proposed framework. The expected output from this research is to enhance the provision of healthcare services during various network failures.
Copyright (c) 2023 O.S. Albahri, Amneh Alamleh , Tahsien Al-Quraishi , Rahul Thakkar https://creativecommons.org/licenses/by/4.0
The Considerations of Trustworthy AI Components in Generative AI; A Letter to Editor
Dear Editor: In navigating the ever-expanding realms of Artificial Intelligence (AI) across diverse domains, particularly within the purviews of Generative AI, it becomes imperative to delve into the intricate considerations of trustworthy AI components [1]. This letter aims to underscore the salient aspects of this discussion with a specific focus on the thematic areas championed by the journal.
Copyright (c) 2023 A. S. Albahri , Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb https://creativecommons.org/licenses/by/4.0
Developing an Electronic Health Records System Based on the National Identity by Using Angular Web Application Framework
Electronic Health Records systems provide a better management system for patients and the healthcare spiciest by providing an accurate, up-to-date, and complete information about patients at the point of care. Further, enabling fast access to patient records for more coordinated, efficient care. The core objective for this study is to connect the national identity with the electronic records for patients. This aims to obtain a secure and accurate identification and authentication of patients and their information as well as will enable information exchange between different healthcare centers. This research presents the design and implementation of an electronic health records (EHR) system with national ID integration to cover the mentioned significant objectives. The system utilizes the following technologies: Angular, HTML, CSS, and Bootstrap for the frontend and NestJS, Prisma, and JWT for the backend, with a Postgres database. The project follows the Agile software development methodology and includes system analysis, design, implementation, testing, and evaluation. The system features separate interfaces for administrators, doctors, and patients with appropriate table relations, endpoints, and schemas. The system was tested using Jest and test Bed for functional, performance, and security testing. The results demonstrate that the EHR system with national ID integration can improve patient record management and access. Limitations and future improvements are also discussed. Overall, this project provides a valuable contribution to the healthcare industry and offers a solid foundation for further development and refinement.
Copyright (c) 2023 Mustafa Mohammed Rasheed, Muneera Alsaedi, Ahmed Al Ibraheemi https://creativecommons.org/licenses/by/4.0
A Frequency-Domain Pattern Recognition Model for Motor Imagery-Based Brain-Computer Interface
Brain-computer interface (BCI) is an appropriate technique for totally paralyzed people with a healthy brain. BCI based motor imagery (MI) is a common approach and widely used in neuroscience, rehabilitation engineering, as well as wheelchair control. In a BCI based wheelchair control system the procedure of pattern recognition in term of preprocessing, feature extraction, and classification plays a significant role in system performance. Otherwise, the recognition errors can lead to the wrong command that will put the user in unsafe conditions. The main objectives of this study are to develop a generic pattern recognition model-based EEG –MI Brain-computer interfaces for wheelchair steering control. In term of preprocessing, signal filtering, and segmentation, multiple time window was used for de-noising and finding the MI feedback. In term of feature extraction, five statistical features namely (mean, median, min, max, and standard deviation) were used for extracting signal features in the frequency domain. In term of feature classification, seven machine learning were used towards finding the single and hybrid classifier for the generic model. For validation, EEG data from BCI Competition dataset (Graz University) were used to validate the developed generic pattern recognition model. The obtained result of this study as the following: (1) from the preprocessing perspective it was seen that the two-second time window is optimal for extracting MI signal feedback. (2) statistical features are seen have a good efficiency for extracting EEG-MI features in the frequency domain. (3) Classification using (MLP-LR) is perfect in a frequency domain based generic pattern recognition model. Finally, it can be concluded that the generic pattern recognition model-based hybrid classifier is efficient and can be deployed in a real-time EEG-MI based wheelchair control system.
Copyright (c) 2024 Z.T. Al-Qaysi , M. S Suzani , Nazre bin Abdul Rashid , Reem D. Ismail , M.A. Ahmed , Rasha A. Aljanabi, Mohd Arfian Ismail https://creativecommons.org/licenses/by/4.0
Interpretative Artificial Intelligence for Brain Tumor Detection and Classification
A brain tumor, a predominant source of unregulated cellular proliferation in the central nervous system, poses significant difficulties in medical detection and therapy. Timely and precise identification is crucial for successful intervention. Early malignancies within adult brains are universally lethal. Recent advancements in artificial intelligence (AI) within the realm of computer vision have facilitated the automated characterization and diagnosis of brain tumor lesions. The precise identification and categorization of brain tumors are essential elements of medical diagnosis. Recently, AI methodologies have gained prominence in improving brain tumor detection. Nonetheless, AI models frequently exhibit a deficiency in transparency, which poses significant challenges in critical domains such as healthcare. This study presents an Explainable AI (XAI) system designed for brain tumor identification, providing doctors with clear and interpretable insights into model decisions. This system utilizes advanced XAI techniques to enhance confidence, reliability, and clinical acceptance of AI-based tumor detection technologies. Creating explainable AI methodologies will be crucial for enhancing human-machine interactions and aiding in the identification of appropriate training techniques. Future endeavors will enhance the dataset and implement discoveries in real-time diagnostic equipment, therefore improving the discipline.
Copyright (c) 2026 Karthik Kumar Vaigandla https://creativecommons.org/licenses/by/4.0
A Hybrid Multi-Factor Authentication System with GPS-Based on SPECK Encryption in Oil Cybersecurity
The oil and gas sector has become increasingly exposed to sophisticated cyberattacks, where classic single- or two-factor authentication mechanisms are insufficient for securing online transactions in oil-and-gas operational environments. This paper proposes a hybrid multi-factor authentication (H-MFA) system that integrates six security components, consisting of four authentication factors (Time-based One-Time Passwords (TOTP), GPS-based location validation, Password Salting and Hashing, and Biometric template factor (simulated) and two cryptographic enablers (SPECK lightweight encryption and Non-Interactive Zero-Knowledge (NIZK)-based privacy-preserving verification). The presented system attempts to reach a desirable trade-off between security assurance and computational feasibility for resource-limited industrial endpoints and edge gateways. Moreover, a Real-or-Random (RoR) inspired security model is incorporated as a theoretical security argument to provide indistinguishability-based validation against inference and distinguishing attacks on authentication outputs. We have implemented the system in Java and evaluated it through repeated experimental runs using an access-behavior dataset under different parameter settings. The evaluation considers metrics such as decision-level determinism, randomness of stochastic token outputs, stability towards repeated experiments, and scalability towards increasing workload and concurrency. We conducted a simulation-based feasibility evaluation using the same access-behavior dataset, where TOTP/GPS/biometric-template and NIZK-related outputs are instantiated via controlled proxies. Security discussion is supported by a RoR-inspired indistinguishability argument under stated assumptions, while engineering performance is evaluated separately using latency, throughput, CPU, and memory. In general, this paper provides an integrated and implemented MFA design that combines factor diversity, privacy-preserving verification, and secure audit-log protection for reliable online transactions authentication in critical oil and gas infrastructures.
Copyright (c) 2026 Amal Khaleel Hamad , Mishall Al-Zubaidie https://creativecommons.org/licenses/by/4.0
Quantum-Inspired FBMC Transceivers for 6G: Potential Applications, Fundamentals, Opportunities, Advantages, Challenges, Future Trends
The forthcoming sixth-generation (6G) networks require transformative improvements in spectral efficiency, latency, and energy performance. This review highlights the novelty of integrating quantum-inspired approaches with Filter Bank Multicarrier (FBMC) transceivers, offering a new paradigm for 6G communication. The fusion of quantum-inspired algorithms with FBMC enables advanced interference management, improved robustness, and adaptability for diverse applications such as intelligent transportation, immersive extended reality, and ultra-reliable low-latency communication. Key challenges, including hardware complexity, scalability, and algorithmic optimization, are critically examined. The article further outlines future research directions, emphasizing how this integration can shape efficient, intelligent, and resilient 6G transceivers.
Strengthening cloud data protection based on a novel cyber security framework
Cybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the IRF algorithm. It optimizes feature importance weights during training and improves the model's ability to detect intrusions in cloud environments accurately. The proposed detection model is implemented in Python software. In the findings assessment phase, we effectively assessed the performance of our proposed DB-IRF in detecting earthquake incidents across multiple evaluation metrics such as Accuracy (97.5%), Precision (97.96%), F1 Score (98.48%) and Recall (97.85%). We also conducted a comparison analysis with other conventional methodologies. Our experimental results demonstrate the capability and reliability of the recommended framework.
Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF).
Copyright (c) 2025 Ammar Mhana , Fadhel K. Jabor, Ghufran A. Omran , Jamal Fadhil Tawfeq , Ahmed Dheyaa Radhi , Varsha K. Harpale , Mrinal Bachute , Pritesh Shah , Ravi Sekhar https://creativecommons.org/licenses/by/4.0
Revolutionizing Wireless Sensor Networks through an Effective Approach for Quality of Service Enhancement
Data Collection of Wireless Sensor Networks (WSN) are integral parts of modern technology in various fields. Nonetheless, due to inherent limitations like energy, bandwidth, and computation, a prominent challenge that arises is providing Quality of service (QoS). WSNs consist of constrained resource networks of sensors (e.g., batteries); thus, routing and deployment techniques are significant for improving the QoS performance of WSNs. We have introduced a new method of High Secure Parallel Particle Swarm Routing Algorithm (HS-PPSRA) in this work to enhance the QoS in WSNs. The approach with the metrics is delta, hypervolume (HV), Result of Multi-objectives via Dynamic Weighting (RMDW), Non-Dominated Solution (NDS) concerning important data transfer, dynamic change of network features, and using adaptive routing algorithms. Results show significant improvements in QoS metrics including reduced latency, increased reliability, and energy utilization. For checking existing algorithms, the proposed HS-PPSRA outperforms past algorithms. This case resolves the issues associated with QoS enhancement and presents a holistic approach to transitioning WSNs
High-Secure Parallel Particle Swarm Routing Algorithm (HS-PPSRA)
Copyright (c) 2025 Adil M. Salman , H. I. Wahhab , Aseel B. Alnajjar , Bourair Al-Attar , Ravi Sekhar , Nilisha Itankar https://creativecommons.org/licenses/by/4.0
A Cognitive Energy-Driven Routing Strategy for Ultra-Efficient Data Transfer in Wireless Sensor Networks
WSNs deploy multi-hop routes to transfer information from distributed nodes to central points because of their established use for environmental inspection and data acquisition. The effective transmission of data plays a critical role in Wireless Sensor Networks particularly in challenging conditions that produce temporary network interruptions leading to data loss. The present body of work faces energy utilization constraints of Pegasus at 75% efficiency alongside scalability limitations at 300 nodes in A-Leach and packet delivery performance at 94% PDR in DSO-EHO. The current work presents a new optimization method called Levy Flight fine-tuned Red Deer Optimization (LFRDO) for route path optimization. Red Deer Optimization and Levy Flight produce an algorithm that optimizes energy expenditure by promoting active exploration techniques to simultaneously minimize network delays and lengthen operational life. The proposed method achieves a 98% reduction in energy usage together with enhanced packet delivery ratio (96% for 500 nodes) at a throughput rate of 0.9 Mbps. The LFRDO simulation shows a 95% energy efficiency level surpassing DSO-EHO at 92% while operating effectively with networks having up to 500 nodes. The system prolongs network operational time by 35% when combined with intelligent routing decisions that minimize end-to-end delay. The proposed method provides solutions to resolve three primary WSN issues concerning scalability together with energy efficiency alongside dependable data transmission during system changes
Levy Flight fine-tuned Red Deer Optimization (LFRDO)
Copyright (c) 2025 Fuqdan A. Al- Ibraheemi , Mushtaq Talb Tally , Safwan Nadweh , Intisar A.M . Al Sayed , Jamal Fadhil Tawfeq , Hassan Muwafaq Gheni, Pritesh Shah , Ravi Sekhar https://creativecommons.org/licenses/by/4.0