Prepared by
Habeeb Ibrahim
Department of Mathematics
College of Science
Qassim University
Buraydah 51452 -Saudi Arabia
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Doi : https://doi.org/10.53348/JSST1ST8

Key words: bounded linear, Rosenthal space, symmetric operator, probability measure

Abstract
We defined Rosenthal Space, and gave description of some Banach spaces those are not containing the space  in terms of holomorphic continuous mappings. The polynomials those are continuous weakly sequentially type, and bounded subsets those are weakly continuous type has been described. Finally, we gave characterizations of bounded linear operators

Prepared by
Suliman Mustafa Mohamed Abakar
 Department of Cybersecurity
College of Computer
Qassim University, Buraydah
Saudi Arabia
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Doi : https://doi.org/10.53348/JSST2ST8

Keywords: Deepfake detection; War zones; Sudan War; Real-time AI; Multimedia forensics; DeepTrace; Audio liveness; Video forensics; Information warfare.

Prepared by

Yousif Altayeb
Department of Management Information Systems
College of Business and Economics
Qassim University
Buraydah, 51452,  Saudi Arabia
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Doi: https://doi.org/10.53348/JSST3ST8

KEYWORDS: Fiber, vector bundles, characteristic classes, cohomology, homology theory, topological, homogeneous space, Sequence Analysis

Abstract

The aim of this paper is to analyze the relationship between fiber bundle theory and characteristic classes in homogeneous spaces. It clarifies the mathematical framework and definitions, utilising tools like classification spaces, homology, and cohomology theory to explain key concepts in algebraic topology. It highlights vector bundle theory as essential for these applications and describes the classification of fiber vector bundles, linking it to the characteristic class theory of cohomology for computations across various homogeneous spaces. Important definitions and properties of general vector bundles, as well as tangential and perpendicular bundles of smooth manifolds, are also discussed

Prepared by

Manal Yagoub Ahmed Juma
Department of Mathematics
College of Science
Qassim University
Buraidah 51452
Saudi Arabia
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Khadiga Wadi Nahar Tajer
Department of Mathematics
College of Science
Qassim University
Buraidah 51452
Saudi Arabia
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Corresponding Author
Manal Yagoub Ahmed Juma
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Doi : https://doi.org/10.53348/JSST4ST8

Key words: Composition operator, Fourier series, topological structure, compactness,Clark measure, fractional derivatives, differential equations, numerical methods, nonlinear equations

Abstract
In this paper, we study the topological structure of the space of composition operators on Hardy–Fourier spaces   and its infinite-dimensional extension
We in particular obtain a number of new results on compactness, Clark measures, and connected components utilizing techniques previously developed for Dirichlet Hardy spaces. Moreover, we show that path connectedness comes from compact differences and give a classification of connected components in terms of Clark measure invariance. This confines the Bohr transform to be a representation of the Dirichlet–Fourier connection, thus giving a unified topological framework for composition-operator spaces linking the two function spaces. We present the first comprehensive study of composition operators in the Hardy–Fourier framework, providing a unified topological characterization through Bohr-transformed invariants. Furthermore, the unified operator–topological framework developed here can naturally interact with broader analytic structures appearing in fractional derivatives, differential equations, nonlinear equations, and numerical methods, thereby enlarging the range of application of these topological invariants beyond the classical Hardy–Fourier context

Prepared by
Galal eldin Abbas Eltayeb
Department of Management Information Systems
College of Business and  Economics
Qassim University
Buraydah, Saudi Arabia
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https://orcid.org/0000-0003-38-2061 

Abdalilah Alhalangy
Department of Computer Engineering
College of Computer
Qassim University
Buraydah, Saudi Arabia
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https://orcid.org/0000-0003-2735-8208 

Doi: https://doi.org/10.53348/JSST5ST8

Keywords:Artificial intelligence, machine learning, deep learning, natural language processing systems, data privacy, network security 

Abstract
But now, given the AI revolution and increased interest in bringing virtual agents and assistants to life banks too are testing LLM-powered AI agents that may assist customers, explain and customize products as well as simplify operational work done by bank employees in the background. But similar systems are susceptible to prompt injection, insecure output handling, and other LLM-specific threats that had only become more prevalent since these publications. Existing surveys and frameworks survey the generic security space of LLMs but do not propose reach an end-to-end, banking-specific threat model nor deployable defense architecture for assistants in line with systems from core into the edge. We also present a data-privacy-aware threat model and a learnable multi-layer defense framework for LLM-based banking assistants. We showcase a reference architecture for an omnichannel banking chatbots built using an instruction-tuned, medium-scale commercial LLM and also access to tools for accessing customer data, payments and knowledge bases. Assets, trust boundaries, adversary capabilities, and a banking-related threat taxonomy based on OWASP LLM guidance in combination with the NIST AI Risk Management Framework and recent sector-wide reports are defined. We then present a defense-in-depth framework with which we employ: governing inputs, prompt level controls, policy enforcement in the orchestration layer, safety guards at the tool-layer, structured output filtering along with response-risk scoring and constant monitoring and governance. We introduce a prototype framework for an effective LLM API designed to mimic digital banking assistant responses and evaluate against synthesized and real-world banking dialogues. This analysis will comprise 48 attack models based on the use of SMS injection techniques, leaking data or other misuses or abuses of authenticated tools. The framework decreases the overall success rates of attacks from 68–79% to between 14–24% across three common styles of attack, while introducing an average response time overhead of less than 11.8%, a modest increase in token consumption and maintaining false alarm rates below 7%. We, too, explore how the results dovetail with the growing regulatory landscape for AI in financial services and how banks can choose to tailor the framework according to their risk appetite and legacy infrastructure constraints

Prepared by
Ahmed Khalid Abdullah
Directorate of Ninawa Health
Ministry of Health, Mosul, Iraq

Raid Rafi Omar Al-Nima
Technical Engineering College for Computer and Artificial
Intelligence/NorthernTechnical University
Mosul, Iraq
Artificial Intelligence Research Center, Northern Technical
University, Mosul, Iraq

Khalid Ghanim Majeed
Department of Medical Physiology
 College of Medicine
Nineveh University, Mosul, Iraq

Ahmed Isam Abdulrhman
Directorate of Ninawa Health
 Ministry of Health, Mosul, Iraq

Marwn Fawzi al-Bayati
Electrical engineering College
Middle Technical University
Bagdad, Iraq

Doi: https://doi.org/10.53348/JSST6ST8

Keywords: Osteoporosis, Deep learning, Bobcat Optimization Algorithm, Convolutional Neural Network

Abstract
Osteoporosis is characterized by diminished bone mass and bone tissue loss, which results in weakening bones, decreasing bone strength and increasing risk of fractures. This paper exploits a Medical Lumber Spine Images (MLSI) dataset of Dual-Energy X-ray Absorptiometry (DEXA) in a clinic in Mosul / Iraq in order to be classified as either normal or having osteoporosis. Here, it is decided to explore use of Bobcat Optimization Algorithm (BOA) for selection of best Convolutional Neural Network (CNN) hyperparameters. The proposed method is called Bobcat Optimization for CNN (BOCNN) model, which systematically explores and optimizes 18 hyper-parameters. It combines the effectiveness of BOA for optimizing effective CNN hyperparameters which can further reduce the manual tuning efforts. This study uses the real clinical data set from the MLSI. The proposed model is able to diagnose Osteoporosis correctly in 95% of the cases from unseen data. The Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) equals to 0.98