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Acadlore Transactions on AI and Machine Learning
ATAIML
Acadlore Transactions on AI and Machine Learning (ATAIML)
ATAMS
ISSN (print): 2957-9562
ISSN (online): 2957-9570
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2024: Vol. 3
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Acadlore Transactions on AI and Machine Learning (ATAIML) aims to spearhead the academic exploration of artificial intelligence, machine, and deep learning, along with their associated disciplines. Underscoring the pivotal role of AI and machine learning innovations in shaping the modern technological landscape, ATAIML strives to decode the complexities of current methodologies and applications in the AI domain. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief(2)
andreas pester
British University in Egypt, Egypt
andreas.pester@bue.edu.eg | website
Research interests: Differential Equations; LabVIEW; MATLAB, Educational Technology; Blended Learning; M-Learning; Deep Learning
zhuang wu
Capital University of Economics and Business, China
wuzhuang@cueb.edu.cn | website
Research interests: Decision Optimization and Management; Computational Intelligence; Intelligent Information Processing; Big Data; Online Public Opinion; Image Processing and Visualization

Aims & Scope

Aims

Acadlore Transactions on AI and Machine Learning (ATAIML) emerges as a pivotal platform at the intersection of artificial intelligence, machine learning, and their multifaceted applications. Recognizing the profound potential of these disciplines, the journal endeavors to unravel the complexities underpinning AI and ML theories, methodologies, and their tangible real-world implications.

In a world advancing at digital light-speed, ATAIML posits that AI and ML reshape industries at their core. From the expansion of reality to the birth of synthetic data and the intricate design of graph neural networks, such advancements are at the forefront of innovation. With a mission to chronicle these paradigm shifts, ATAIML aims to serve as a beacon for researchers, professionals, and enthusiasts eager to fathom the vast horizons of AI and ML in the modern age.

Furthermore, ATAIML highlights the following features:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

ATAIML's expansive scope encompasses, but is not limited to:

  • AI-Integrated Sensory Technologies: Insights into AI's role in amplifying and harmonizing sensory data.

  • Symbiosis of AI and IoT: The collaborative dance between artificial intelligence and the Internet of Things and their cumulative impact on contemporary society.

  • Mixed Realities Shaped by AI: Probing the AI-crafted mixed-reality realms and their implications.

  • Sustainable AI Innovations: A focus on 'Green AI' and its instrumental role in shaping a sustainable future.

  • Synthetic Data in the AI Era: A deep dive into the rise and relevance of synthetic data and its AI-driven generation.

  • Graph Neural Paradigms: Exploration of the nuances of graph-centric neural networks and their evolutionary trajectory.

  • Interdisciplinary AI Applications: Delving into AI's intersections with fields such as psychology, fashion, and the arts.

  • Moral and Ethical Dimensions of AI: A comprehensive study of the ethical landscapes carved by AI's advancements and the corresponding legal challenges.

  • Diverse Learning Methodologies: Exploration of revolutionary learning techniques ranging from Bayesian paradigms to statistical approaches in ML.

  • Emergent AI Narratives: Spotlight on cutting-edge AI technologies, foundational standards, computational attributes, and their transformative use cases.

  • Holistic Integration: Emphasis on multi-disciplinary submissions that combine insights from varied fields, offering a holistic perspective on AI and ML's global resonance.

Articles
Recent Articles
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Open Access
Research article
Characterization and Risk Assessment of Cyber Security Threats in Cloud Computing: A Comparative Evaluation of Mitigation Techniques
oludele awodele ,
chibueze ogbonna ,
emmanuel o. ogu ,
johnson o. hinmikaiye ,
jide e. t. akinsola
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Available online: 05-15-2024

Abstract

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Advancements in information technology have significantly enhanced productivity and efficiency through the adoption of cloud computing, yet this adoption has also introduced a spectrum of security threats. Effective cybersecurity mitigation strategies are imperative to minimize the impact on cloud infrastructure and ensure reliability. This study seeks to categorize and assess the risk levels of cybersecurity threats in cloud computing environments, providing a comprehensive characterization based on eleven major causes, including natural disasters, loss of encryption keys, unauthorized login access, and others. Using fuzzy set theory to analyze uncertainties and model threats, threats were identified, prioritized, and categorized according to their impact on cloud infrastructure. A high level of data loss was revealed in five key features, such as encryption key compromise and unauthorized login access, while a lower impact was observed in unknown cloud storage and exposure to sensitive data. Seven threat features, including encryption key loss and operating system failure, were found to significantly contribute to data breaches. In contrast, others like virtual machine sharing and impersonation, exhibited lower risk levels. A comparative analysis of threat mitigation techniques determined Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service and Elevation of Privilege (STRIDE) as the most effective methodology with a score of 59, followed by Quality Threat Modeling Methodology (QTMM) (57), Common Vulnerability Scoring System (CVSS) (51), Process for Attack Simulation and Threat Analysis (PASTA) (50), and Persona non-Grata (PnG) (47). Attack Tree and Hierarchical Threat Modeling Methodology (HTMM) each achieved 46, while Linkability, Identifiablility, Nonrepudiation, Detectability, Disclosure of Information, Unawareness and Noncompliance (LINDDUN) scored 45. These findings underscore the value of fuzzy set theory in tandem with threat modeling to categorize and assess cybersecurity risks in cloud computing. STRIDE is recommended as an effective modeling technique for cloud environments. This comprehensive analysis provides critical insights for organizations and security experts, empowering them to proactively address recurring threats and minimize disruptions to daily operations.

Abstract

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Dental implants (DIs) are prone to failure due to uncommon mechanical complications and fractures. Precise identification of implant fixture systems from periapical radiographs is imperative for accurate diagnosis and treatment, particularly in the absence of comprehensive medical records. Existing methods predominantly leverage spatial features derived from implant images using convolutional neural networks (CNNs). However, texture images exhibit distinctive patterns detectable as strong energy at specific frequencies in the frequency domain, a characteristic that motivates this study to employ frequency-domain analysis through a novel multi-branch spectral channel attention network (MBSCAN). High-frequency data obtained via a two-dimensional (2D) discrete cosine transform (DCT) are exploited to retain phase information and broaden the application of frequency-domain attention mechanisms. Fine-tuning of the multi-branch spectral channel attention (MBSCA) parameters is achieved through the modified aquila optimizer (MAO) algorithm, optimizing classification accuracy. Furthermore, pre-trained CNN architectures such as Visual Geometry Group (VGG) 16 and VGG19 are harnessed to extract features for classifying intact and fractured DIs from panoramic and periapical radiographs. The dataset comprises 251 radiographic images of intact DIs and 194 images of fractured DIs, meticulously selected from a pool of 21,398 DIs examined across two dental facilities. The proposed model has exhibited robust accuracy in detecting and classifying fractured DIs, particularly when relying exclusively on periapical images. The MBSCA-MAO scheme has demonstrated exceptional performance, achieving a classification accuracy of 95.7% with precision, recall, and F1-score values of 95.2%, 94.3%, and 95.6%, respectively. Comparative analysis indicates that the proposed model significantly surpasses existing methods, showcasing its superior efficacy in DI classification.

Abstract

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Named Entity Recognition (NER), a pivotal task in information extraction, is aimed at identifying named entities of various types within text. Traditional NER methods, however, often fall short in providing sufficient semantic representation of text and preserving word order information. Addressing these challenges, a novel approach is proposed, leveraging dual Graph Neural Networks (GNNs) based on multi-feature fusion. This approach constructs a co-occurrence graph and a dependency syntax graph from text sequences, capturing textual features from a dual-graph perspective to overcome the oversight of word interdependencies. Furthermore, Bidirectional Long Short-Term Memory Networks (BiLSTMs) are utilized to encode text, addressing the issues of neglecting word order features and the difficulty in capturing contextual semantic information. Additionally, to enable the model to learn features across different subspaces and the varying degrees of information significance, a multi-head self-attention mechanism is introduced for calculating internal dependency weights within feature vectors. The proposed model achieves F1-scores of 84.85% and 96.34% on the CCKS-2019 and Resume datasets, respectively, marking improvements of 1.13 and 0.67 percentage points over baseline models. The results affirm the effectiveness of the presented method in enhancing performance on the NER task.

Open Access
Review article
Advances in Breast Cancer Segmentation: A Comprehensive Review
ayah abo-el-rejal ,
shehab eldeen ayman ,
farah aymen
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Available online: 03-20-2024

Abstract

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The diagnosis and treatment of breast cancer (BC) are significantly subject to medical imaging techniques, with segmentation being crucial in delineating pathological regions for precise diagnosis and treatment planning. This comprehensive analysis explores a variety of segmentation methodologies, encompassing classical, machine learning, deep learning (DL), and manual segmentation, as applied in the medical imaging field for BC detection. Classical segmentation techniques, which include edge-driven and threshold-driven segmentation, are highlighted for their utilization of filters and region-based methods to achieve precise delineation. Emphasis is placed on the establishment of clear guidelines for the selection and comparison of these classical approaches. Segmentation through machine learning is discussed, encompassing both unsupervised and supervised techniques that leverage annotated images and pathology reports for model training, with a focus on their efficacy in BC segmentation tasks. DL methods, especially models such as U-Net and convolutional neural networks (CNNs), are underscored for their remarkable efficiency in segmenting BC images, with U-Net models noted for their minimal requirement for annotated images and achieving accuracy levels up to 99.7%. Manual segmentation, though reliable, is identified as time-consuming and susceptible to errors. Various metrics, such as Dice, F-score, Intersection over Union (IOU), and Area Under the Curve (AUC), are used for assessing and comparing the segmentation techniques. The analysis acknowledges the challenges posed by limited dataset availability, data range inadequacy, and confidentiality concerns, which hinder the broader integration of segmentation methods into clinical practice. Solutions to overcome these challenges are proposed, including the promotion of partnerships to develop and distribute extensive datasets for BC segmentation. This approach would necessitate the pooling of resources from multiple organizations and the adoption of anonymization techniques to safeguard data privacy. Through this lens, the analysis aims to provide a thorough analysis of the practical implications of segmentation methods in BC diagnosis and management, paving the way for future advancements in the field.

Open Access
Research article
Enhancing Melanoma Skin Cancer Diagnosis Through Transfer Learning: An EfficientNetB0 Approach
rashmi ashtagi ,
pramila vasantrao kharat ,
vinaya sarmalkar ,
sridevi hosmani ,
abhijeet r. patil ,
afsha imran akkalkot ,
adithya padthe
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Available online: 03-13-2024

Abstract

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Skin cancer, a significant health concern globally, necessitates innovative strategies for its early detection and classification. In this context, a novel methodology employing the state-of-the-art EfficientNetB0 deep learning architecture has been developed, aiming to augment the accuracy and efficiency of skin cancer diagnoses. This approach focuses on automating the classification of skin lesions, addressing the challenges posed by their complex structures and the subjective nature of conventional diagnostic methods. Through the adoption of advanced training techniques, including adaptive learning rates and Rectified Adam (RAdam) optimization, a robust model for skin cancer classification has been constructed. The findings underscore the model's capability to achieve convergence during training, illustrating its potential to transform dermatological diagnostics significantly. This research contributes to the broader fields of medical imaging and artificial intelligence (AI), underscoring the efficacy of deep learning in enhancing diagnostic processes. Future endeavors will explore the realms of explainable AI (XAI), collaboration with medical professionals, and adaptation of the model for telemedicine, ensuring its continued relevance and applicability in the dynamic landscape of skin cancer diagnosis.

Open Access
Research article
Enhanced Color Image Encryption Utilizing a Novel Vigenere Method with Pseudorandom Affine Functions
hamid el bourakkadi ,
abdelhakim chemlal ,
hassan tabti ,
mourad kattass ,
abdellatif jarjar ,
abdellhamid benazzi
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Available online: 03-13-2024

Abstract

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In the realm of digital image security, this study presents an innovative encryption methodology for color images, significantly advancing the traditional Vigenere cipher through the integration of two extensive pseudorandom substitution matrices. These matrices are derived from chaotic maps widely recognized for their cryptographic utility, specifically the logistic map and the skew tent map, chosen for their straightforward implementation capabilities in encryption systems and their high sensitivity to initial conditions. The process commences with the vectorization of the original image and the computation of initial values to alter the starting pixel's value, thereby initiating the encryption sequence. A novel aspect of this method is the introduction of a Vigenere mechanism that employs dynamic pseudorandom affine functions at the pixel level, enhancing the cipher's robustness. Subsequently, a comprehensive permutation strategy is applied to bolster the vector's integrity and elevate the temporal complexity against potential cryptographic attacks. Through simulations conducted on a varied collection of images, encompassing different sizes and formats, the proposed encryption technique demonstrates formidable resilience against both brute-force and differential statistical attacks, thereby affirming its efficacy and security in safeguarding digital imagery.

Open Access
Research article
Enhanced Real-Time Facial Expression Recognition Using Deep Learning
hafiz burhan ul haq ,
waseem akram ,
muhammad nauman irshad ,
amna kosar ,
muhammad abid
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Available online: 01-24-2024

Abstract

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In the realm of facial expression recognition (FER), the identification and classification of seven universal emotional states, surprise, disgust, fear, happiness, neutrality, anger, and contempt, are of paramount importance. This research focuses on the application of convolutional neural networks (CNNs) for the extraction and categorization of these expressions. Over the past decade, CNNs have emerged as a significant area of research in human-computer interaction, surpassing previous methodologies with their superior feature learning capabilities. While current models demonstrate exceptional accuracy in recognizing facial expressions within controlled laboratory datasets, their performance significantly diminishes when applied to real-time, uncontrolled datasets. Challenges such as degraded image quality, occlusions, variable lighting, and alterations in head pose are commonly encountered in images sourced from unstructured environments like the internet. This study aims to enhance the recognition accuracy of FER by employing deep learning techniques to process images captured in real-time, particularly those of lower resolution. The objective is to augment the accuracy of FER in real-world datasets, which are inherently more complex and collected under less controlled conditions, compared to laboratory-collected data. The effectiveness of a deep learning-based approach to emotion detection in photographs is rigorously evaluated in this work. The proposed method is exhaustively compared with manual techniques and other existing approaches to assess its efficacy. This comparison forms the foundation for a subjective evaluation methodology, focusing on validation and end-user satisfaction. The findings conclusively demonstrate the method's proficiency in accurately recognizing emotions in both laboratory and real-world scenarios, thereby underscoring the potential of deep learning in the domain of facial emotion identification.

Open Access
Research article
Adaptive Lane Keeping Assistance System with Integrated Driver Intent and Lane Departure Warning
haigang wei ,
wei tong ,
yueyong jiang ,
jianlu li ,
ramesh vatambeti
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Available online: 01-21-2024

Abstract

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The development of an adaptive Lane Keeping Assistance System (LKAS) is presented, focusing on enhancing vehicular lateral stability and alleviating driver workload. Traditional LKAS with static parameters struggle to accommodate varying driver behaviors. Addressing this challenge, the proposed system integrates adaptive driver characteristics, aligning with individual driving habits and intentions. A novel lane departure decision model is introduced, employing time-space domain fusion to effectively discern driver's lane change intentions, thus informing system decisions. Further innovation is realized through the application of reinforcement learning theory, culminating in the creation of a master controller for lane departure intervention. This controller dynamically adjusts to driver behavior, optimizing lane keeping accuracy. Extensive simulations, coupled with hardware-in-the-loop experiments using a driving simulator, substantiate the system's efficacy, demonstrating marked improvements in lane keeping precision. These advancements position the system as a significant contribution to the field of driver assistance technologies.

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In this study, an integrated pest and disease recognition system for agricultural drones has been developed, leveraging deep learning technologies to significantly improve the accuracy and efficiency of pest and disease detection in agricultural settings. By employing convolutional neural networks (CNN) in conjunction with high-definition image acquisition and wireless data transmission, the system demonstrates proficiency in the effective identification and classification of various agricultural pests and diseases. Methodologically, a deep learning framework has been innovatively applied, incorporating critical modules such as image acquisition, data transmission, and pest and disease identification. This comprehensive approach facilitates rapid and precise classification of agricultural pests and diseases, while catering to the needs of remote operation and real-time data processing, thus ensuring both system efficiency and data security. Comparative analyses reveal that this system offers a notable enhancement in both accuracy and response time for pest and disease recognition, surpassing traditional detection methods and optimizing the management of agricultural pests and diseases. The significant contribution of this research is the successful integration of deep learning into the domain of agricultural pest and disease detection, marking a new era in smart agriculture technology. The findings of this study bear substantial theoretical and practical implications, advancing precision agriculture practices and contributing to the sustainability and efficiency of agricultural production.

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In the domain of intellectual property protection, the embedding of digital watermarks has emerged as a pivotal technique for the assertion of copyright, the conveyance of confidential messages, and the endorsement of authenticity within digital media. This research delineates the implementation of a non-blind watermarking algorithm, utilizing alpha blending facilitated by discrete wavelet transform (DWT) to embed watermarks into genuine images. Thereafter, an extraction process, constituting the inverse of embedding, retrieves these watermarks. The robustness of the embedded watermark against prevalent manipulative attacks, specifically median filter, salt and pepper (SAP) noise, Gaussian noise, speckle noise, and rotation, is rigorously evaluated. The performance of the DWT-based watermarking is quantified using the peak signal-to-noise ratio (PSNR), an objective metric reflecting fidelity. It is ascertained that the watermark remains tenaciously intact under such adversarial conditions, underscoring the proposed method's suitability for applications in digital image security and copyright verification.

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This investigation delineates an optimised predictive model for employee attrition within a substantial workforce, identifying pertinent models tailored to the specific context of employee and organisational variables. The selection and refinement of the appropriate predictive model serve as cornerstones for enhancements and updates, which are integral to honing the model's precision in prognosticating potential departures. Through meticulous optimisation, the model demonstrates proficiency in pinpointing the pivotal factors contributing to employee turnover and elucidating the interdependencies among salient variables. A suite of 27 general and eight critical variables were scrutinised. Pertinent correlations were unearthed, notably between monthly income and job satisfaction, home-to-work distance and job satisfaction, as well as age with both job satisfaction and performance metrics. Drawing from prior studies in analogous domains, a three-stage analytical methodology encompassing data exploration, model selection, and implementation was employed. The rigorous training of the optimised model encompassed both attrition factors and variable correlations, culminating in predictive outcomes with a precision of 90% and an accuracy of 87%. Implementing the refined model projected that 113 out of 709 employees, equating to 15.93%, were at a heightened risk of exiting the organisation. This quantitative foresight equips stakeholders with a strategic tool for preemptive interventions to mitigate turnover and sustain organisational vitality.

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In the realm of agriculture, crop yields of fundamental cereals such as rice, wheat, maize, soybeans, and sugarcane are adversely impacted by insect pest invasions, leading to significant reductions in agricultural output. Traditional manual identification of these pests is labor-intensive and time-consuming, underscoring the necessity for an automated early detection and classification system. Recent advancements in machine learning, particularly deep learning, have provided robust methodologies for the classification and detection of a diverse array of insect infestations in crop fields. However, inaccuracies in pest classification could inadvertently precipitate the use of inappropriate pesticides, further endangering both agricultural yields and the surrounding ecosystems. In light of this, the efficacy of nine distinct pre-trained deep learning algorithms was evaluated to discern their capability in the accurate detection and classification of insect pests. This assessment utilized two prevalent datasets, comprising ten pest classes of varied sizes. Among the transfer learning techniques scrutinized, adaptations of ResNet-50 and ResNet-101 were deployed. It was observed that ResNet-50, when employed in a transfer learning paradigm, achieved an exemplary classification accuracy of 99.40% in the detection of agricultural pests. Such a high level of precision represents a significant advancement in the field of precision agriculture.

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