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 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.
Copyright (c) 2026 Karthik Kumar Vaigandla https://creativecommons.org/licenses/by/4.0
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
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
Assessing the Impact of Quantum Computing on Data Encryption Practices and Information Security
Encryption of data is a cornerstone of information security with confidentiality, integrity, and availability of sensitive information. However, the advent of quantum computing (QC) adds complexity to the classical encryption mechanisms by threatening their quantum resilience. This work looks into incorporating QC into cryptographic schemes through the evolution of an Elliptic Quantum Cryptosystem (EQC), fusing elliptic curve cryptography (ECC) and quantum annealing. The objective is to offer protection against attacks in conventional cryptographic schemes and enhance quantum attack resistance. Quantum annealing maximizes encryption through quantum fluctuations to locate best solutions, resulting in higher efficiency and reliability. The research examines top performance measures, including encryption time, decryption time, bit error rate (BER), computational efficiency, level of security, and scalability. Experimental results confirm that the proposed EQC technique has improved performance compared to existing approaches. Specifically, it takes 35 ms encryption time, 40 ms decryption time, 0.0005% BER, 80 seconds computational complexity, 9 level of security, and graded scalability as 8. These outcomes confirm the efficiency of the approach in enhancing encryption security and adaptability against quantum attacks. The problem addressed is the vulnerability of classical encryption systems to quantum advancements. By the addition of quantum-safe procedures, EQC provides a safe alternative, ensuring data confidentiality and integrity during the era of quantum. Disadvantages are the challenge in using quantum annealing and scalability problems for larger datasets. Future researches need to focus on the optimization of quantum approaches, addressing scalability issues, and studying real-world applications to ensure the generalizability of this new approach.
Copyright (c) 2025 Zinah Tareq Nayyef , Najwan Abed Hasan , Yasir A. F. Alaabedi , Mayasa M. Abdulrahman , J. F. Tawfeq , Ahmed Dheyaa Radhi https://creativecommons.org/licenses/by/4.0
The Role of Artificial Intelligence (AI) in Transforming Small and Medium Enterprises (SMEs): A Narrative Review
AI is of great interest to researchers and practitioners as a means of achieving the necessary progress in the business industry. However, the role of AI in transforming SMEs is not well documented. The study assessed the role of AI in transforming SMEs globally. The study investigated the current state of AI in SMEs, challenges, and opportunities. This study reviewed a total of 1,021 published articles, mainly from 1992-2024. The review was performed using scientifically cited and indexed databases, namely Dimensions, Web Science, Elsevier Scopus, and Google Scholar. The study demonstrates how AI enables SMEs to improve competitiveness, streamline operations, and conform to sustainability objectives by tackling particular issues such as scarce resources, operational inefficiencies, and cyber threats. The study closes knowledge gaps in how SMEs, particularly those with limited resources, might benefit from affordable AI tools and platforms. Also, it was found that building workforce capacity through collaborations and customized training programs can help close the skills gap, while improving cybersecurity and implementing efficient data management frameworks can help with privacy issues. However, despite the growing frame of literature on AI packages, studies specializing in AI embracing on the organizational level stay restrained. The study findings emphasized regional integration within the EAC through technology transfer and the development of SME capability. The current study aligns with Uganda’s NDPIII (2020/21–2024/25), under the innovation and technology application pillar, accelerating industrial growth.
Copyright (c) 2025 Justin Mugisha, Akena Francis Adyanga, Johnson Ocan https://creativecommons.org/licenses/by/4.0
Predictive Modeling and Analysis of Monkeypox Outbreaks Using Machine Learning Techniques
As Monkeypox becomes a prevalent public health issue, it is important to develop advanced detection and prediction methods that will inform public health strategies that govern Monkeypox prevention. This study employs machine learning methods to analyze and predict Monkeypox case trends. In particular, features on new cases and deaths were applied to regression and classification models to predict the total number of Monkeypox cases and new case probablity. The regression models that were applied included Linear regression (LR), Decision Tree Regression (DT), Random Forest Regression (RF), Support Vector Regression (SVR), and K-Nearest Neighbor Regression (KNN), with total cases as the outcome. Among regression methods, the Random Forest Regression model performed the best with a Mean Squared Error (MSE) of 92,425,437.81 and R-squared of 0.06, indeicating moderate predictive ability. The methods were also similar to predict new cases, and once again the same algorithms were applied to classification methods, including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) classification, and each model achieved an accuracy score of one (1.00), indicating no new cases would be missed. These results provide evidence that these are effective machine learning methods, and random forests in particular provides the best predictive capability for Monkeypox case trend analysis. The results illustrate how these models can assist data-driven decisions in public health, and evidence-based preparedness and response for future Monkeypox outbreaks.
Copyright (c) 2025 Rand Mohanad Maher , Saba Hussein Rashid , Mustafa Abdulfattah Habeeb , Yahya Layth Khaleel , Fatimah N. Ameen https://creativecommons.org/licenses/by/4.0
The Evolution of Computational Linguistics: A Bibliometric Analysis of Research Trends from 1966 to 2023
This comprehensive bibliometric analysis scrutinizes the evolution of computational linguistics from 1966 to 2023, employing Scopus and specialized software. Findings unveil a noticeable surge in scientific output post-mid-2000s, coinciding with heightened citations, indicating a strong correlation between research output and impact. Key conferences and journals significantly disseminate research, while authorship patterns exhibit diverse scholarly contributions, depicting both consistent and sporadic impact. The identification of recurring themes emphasizes interdisciplinary convergence. Furthermore, the collaborative network analysis delineates dominant countries like the United States, the United Kingdom, and Germany, actively engaged due to prolific research output and extensive collaborations. This emphasizes varying country involvement, offering insights into future interdisciplinary collaboration for advancing computational linguistics.
Copyright (c) 2025 Mohanad G. Yaseen , Hussein Alkattan , Laith Farhan https://creativecommons.org/licenses/by/4.0
Integration of Artificial Intelligence, Blockchain, and Quantum Cryptography for Securing the Industrial Internet of Things (IIoT): Recent Advancements and Future Trends
The swift growth of the Industrial Internet of Things (IIoT) offers tremendous potential to boost productivity, facilitate real-time decision-making, and automate procedures in various industries. However, as industries increasingly adopt IIoT, they face paramount data security, privacy, and system integrity challenges. Artificial intelligence (AI), Blockchain, and quantum cryptography are gaining significant attention as solutions to address these challenges. This paper comprehensively surveys advanced technologies and their potential applications for securing IIoT ecosystems. It reviews findings from 196 sources, including peer-reviewed journal articles, conference papers, books, book chapters, reports, and websites published between 2021 and 2025. The survey draws insights from leading platforms like Springer Nature, ACM Digital Library, Frontiers, Wiley Online Library, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. This paper explores AI-driven approaches to anomaly detection, predictive maintenance, and adaptive security mechanisms, demonstrating how machine learning (ML) and deep learning (DL) can identify and mitigate threats instantly. It also examines Blockchain technology, emphasizing its decentralized nature, immutability, and ability to secure data sharing and authentication within IIoT networks. The paper discusses quantum cryptography, which utilizes quantum mechanics for theoretically unbreakable encryption, ensuring secure communications in highly sensitive industrial environments. The integration of these technologies is analyzed to create a multi-layered defense against cyber threats, highlighting challenges in scalability, interoperability, and computational overhead. Finally, the paper reviews the current research, limitations and challenges, and future directions for securing IIoT with these advanced technologies. This survey offers valuable insights to researchers, engineers, and industry practitioners working to secure the expanding IIoT infrastructure.
Copyright (c) 2025 Guma Ali , Ayodeji Olalekan Salau, Jenan Ayad , Maad M. Mijwil , Malik Sallam , Asiku Denis , Wamusi Robert , Adebo Thomas , Zaward Morish , Simon Peter Kabiito , Aziku Samuel , Klodian Dhoska https://creativecommons.org/licenses/by/4.0
Bibliometric Analysis of Generative AI and Large Language Models in the Scopus Database: Trends, Insights, and Research Landscape
This bibliometric study explores the scientific research landscape by analyzing journal sources, author productivity, institutional contributions, and national research output using bibliometric laws such as Bradford’s Law and Lotka’s Law. The findings identify IEEE ACCESS as the most influential journal, with 26 articles, dominating Zone 1 publications. Lotka’s Law is validated as 94% of authors contributed only one article, while a small group of researchers produced multiple influential works. Institutional analysis shows that the University of California, Cornell University, and Nanyang Technological University significantly increased their research output over time. At the national level, the USA leads with 238 publications, followed by China (77), India (69), and the UK (61). While these results highlight the major contributors to the field, the study also discusses challenges such as data limitations, citation lag effects, and geographical concentration of research efforts. This analysis provides a comprehensive overview of current trends, aiding researchers and policymakers in understanding the dynamics of scientific productivity and influence.
Copyright (c) 2025 Ruchi Doshi, Akhmed Kaleel https://creativecommons.org/licenses/by/4.0
Limitations of Deep Learning vs. Human Intelligence: Training Data, Interpretability, Bias, and Ethics
Deep Learning (DL) has brought a paradigm shift in innumerable fields and allowed machines to learn and decide to a very high extent. Its advantage is that it can analyze large sets of data, identify rather complex patterns, and learn from experience. DL models are widely used to perform complicated tasks [1], [2]. The state of the art in DL includes neural networks with two or more tiers and it has made progressive improvements in fields like image and voice identification, writing comprehension, language generation, and even decision-making self-governing systems [3]. These have led to growing concern and anticipation in the efficiency and rate of change that AI can bring about in industries and people [4].
Copyright (c) 2025 Yahya Layth Khaleel , Mustafa Abdulfattah Habeeb , Thierry Oscar Codjo EDOH https://creativecommons.org/licenses/by/4.0
Explainable AI: Methods, Challenges, and Future Directions
As artificial intelligence (AI)[1] systems become increasingly complex and pervasive, the need for transparency and interpretability has become a critical concern. Explainable AI (XAI)[2, 3] seeks to bridge the gap between opaque machine learning models and human users by providing insights into the decision-making processes of AI systems. This editorial explores the various methods employed in XAI, the challenges faced in achieving interpretability, and potential future directions for the field. The rapid adoption of AI in critical domains such as healthcare, finance, and criminal justice has raised concerns about the "black-box" nature of many AI models[4]. While these models often achieve high accuracy, their decision-making processes remain obscure, making it difficult to diagnose errors, ensure fairness, and build user trust. Explainable AI aims to address these concerns by developing techniques that offer transparency and interpretability without compromising performance.
Copyright (c) 2025 Ahmed Hussein Ali , Marwan Ali Shnan https://creativecommons.org/licenses/by/4.0
From 1G to 6G: Review of history of Wireless Technology Development, Architecture, Applications, and Challenges
Understanding the concentrations of Carbon Dioxide (CO2) and greenhouse gases is very important in solving the problem of climate change. The advent of 6G technology will point to the start of a new revolution in wireless communication and networking. There have been great achievements in terms of networks and transmission of information. This review article provides a comprehensive assessment of 6G, it is equally important to track the evolution process of this technology starting from the conceptual stage up to now. This article explores new features that may potentially transform the global landscape for 6G; identifies major historical events that have shaped the evolution of 6G and stressing the importance equity in terms of technological enhancements and novelties that define it from the preceding generations. Although 6G has promise, it also faces issues, which includes the scarcity of spectrum, and the need for relatively more complex equipment and software integration and the call for enhanced energy efficiency. And goes on to understand the use of 6G, such as reliable and high-speed transmission without delay, transmission between a large number of machines. This review is to provide an analysis of 6G and to take a look at all the features in order to understand the new technology. trend and how it affects various fields in the society.
Copyright (c) 2024 Rozin Badeel, Maryam Abdal , Rehem A. Ahmed , Habil H Mohamed https://creativecommons.org/licenses/by/4.0
Global Analysis and Prediction of CO2 and Greenhouse Gas Emissions across Continents
Understanding the concentrations of Carbon Dioxide (CO2) and greenhouse gases is very important in solving the problem of climate change. These emissions are the major cause of global warming, which, in turn, has many effects on the environment, economy and society. For this reason, the prediction models for these emissions must be precise to aid policy makers in planning for the effects of the climate in the future. To evaluate the emission data of different continents, this paper seeks to identify related patterns and findings that can help reduce emissions worldwide. The dataset used contains emission data and geographic information from several countries and allows the comparison of several ML models. The models that have been reviewed in this study are linear regression (LR), decision tree regression (DT), random forest regression (RF), support vector regression (SVR), k-nearest neighbor regression (KNN), the XGB regressor, the gradient boosting regressor, Ridge and Lasso. Among the models, the gradient boosting regressor was found to have the best prediction capability, with an R-squared value of 0. The highest value of the mean absolute error (MAE) was 929, and the lowest mean squared error (MSE) was 2535.30. This model outperforms the other models because of its excellent ability to identify the complex interactions between the input variables and emissions. The conclusions stress the possibility of using ensembles, such as gradient boosting, for emission forecasting and present a contribution to studies of this issue for researchers and policymakers. This is a nominal attempt in the ongoing global endeavour to gain insight and curb the determinable levels of CO2 and greenhouse gas emissions for effective decision-maki
Copyright (c) 2024 Fadya A. Habeeb, Mustafa Abdulfattah Habeeb , Yahya Layth Khaleel , Fatimah N. Ameen 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
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
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
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
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
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
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
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
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