Heart disease has been the leading cause of mortality globally. The necessity for quick access to trustworthy, dependable, and practical processes for early diagnosis and disease management pertains to numerous risk factors for heart disease. In the current global environment, detecting heart disease through early-onset manifestations is challenging. This has the potential to be fatal if not stopped in time. In isolated, semiurban, or rural locations without access to heart specialists, accurate risk prediction and analysis may be essential for the early detection of cardiac issues. Artificial Intelligence (AI) and robotics are currently used in medical research. This addresses the urgent need for better ways to find, diagnose, and treat heart disease. To close the gap between theory and reality, we offer a dataset on cardiovascular disease that has been carefully put together. The variables in the dataset are age, gender, subtypes, symptoms, risk factors, and result variables that can be either 1 or 0. The

The presence of biases has been demonstrated in a wide range of machine learning applications, yet it is not yet widespread in the case of geospatial datasets. This manuscript illustrates the importance of auditing geospatial datasets for biases with a particular focus on disaster risk management applications, as lack of local data may direct humanitarian actors to utilize global building datasets to estimate damage and the distribution of aid efforts. It is important to ensure there are no biases against the representation of vulnerable populations and that they are not missed in the distribution of aid. This manuscript audits four global building datasets (Google Open Buildings, Microsoft Bing Maps Building Footprints, Overture Maps Foundation, and OpenStreetMap) for biases with regard to Relative Wealth Index, population density, urban/rural proportions, and building size in Tanzania and the Philippines. Dataset accuracies for these two countries are lower than expected. Google Open Buildings (with a confidence above 0.7) and OpenStreetMap demonstrated the best combinations of False Negative and False Discovery, though Google Open Buildings was more consistent across tiles. The equality of opportunity was lowest for the urban/rural proportions, whereas OpenStreetMap and Overture Maps Foundation displayed particularly low equality of opportunity for population density and RWI in Tanzania. These results demonstrate that there are biases in these geospatial datasets. The types of biases are not consistent across datasets and the two study areas which emphasizes the importance of auditing these datasets for biases for new applications and study areas.Note This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

This work presents a new seven-dimensional (7D) chaotic system based on a fluxcontrolled memristor, which we consider in two versions. The first version of a 7D memristive chaotic system describes self-excited attractors. In the second version, in a 7D memristive chaotic system, hidden attractors can arise by adjusting the values of one constant parameter. A study was conducted on both types of innovative 7D dynamic systems to determine Lyapunov exponents, construct bifurcation diagrams, and identify equilibrium points and the corresponding stability conditions for each system. As a result of computer modeling of 7D hyperchaotic systems in Matlab-Simulink, phase portraits were obtained for self-excited and hidden strange attractors. Finally, electronic circuits for new 7D chaos generators with different types of attractors were built using Multisim software, which showed behavior similar to the Matlab-Simulink models. The synchronization problem for both identical 7D hyperchaotic memristive systems was investigated using the active control method. For practical applications, a method of chaotic encryption and decryption of an information signal was developed and numerically tested.

The two main issues in the area of the Internet of Things are low resources consumption and secure data transmission. Conjugating both is fairly hard on ensuring security, leading to great efforts in research. The standard cryptography methods currently proposed are based on simplifications of standard protocols, but are still demanding on resources. Chaotic encryption is a way to reduce this burden, while keeping an equivalent level of security. In this paper, we propose, for the first time, a purely digital scheme for chaotic secure communications, able to be implemented in hardware or software without occupying most of the available resources. Next to the delivered analysis of the system, the experimental demonstration on both FPGA and ESP32 Arduino platforms of chaotic synchronization between transmitter and receiver, included examples of applied encrypted communication in the case of consecutive picture and text transmissions.

Quality assurance in manufacturing often involves manual interventions; however, the integration of machine learning and artificial intelligence can facilitate automation in quality inspection. Industrial process automation significantly contributes to the success of large-scale industries by improving operational efficiency and reallocating resources towards customer satisfaction, thereby enhancing overall business performance. This paper introduces a Convolutional Neural Network (CNN) model based on transfer learning for identifying surface irregularities on finished wooden objects, including stains, porosity, color mismatches, cracks, and knots. Unlike traditional methods that utilize intensity or colored images, our model employs image gradients based on the Canny operator. Edge detection is applied to raw colored images during the image augmentation process to accentuate structural changes on the objects. The rationale behind developing the model on edge-detected images is to reduce noise by eliminating natural patterns that could be misinterpreted as surface irregularities. Deep Convolutional Neural Network models are trained on these edge-detected images. The developed model is assessed using an out-ofsample dataset, with the F1-Score and Accuracy as the primary evaluation metrics. Initially, the model faced challenges in out-of-sample validation due to a low volume of defective images. However, post-performing class balancing, the model exhibited exceptional performance, highlighting its effectiveness in wooden surface irregularity identification.

Alzheimer's disease (AD) is a chronic, degenerative brain disease that affects memory, thinking, and retention. Early diagnosis of AD is essential for effective therapy before clinical symptoms. Positron Emission Tomography (PET) measures the decline in glucose concentration in the temporoparietal association cortex. By identifying meaningful features in medical images, deep learning is an artificial intelligence technology used to identify and predict disease. A convolutional neural network (CNN) is an example of an effective application of deep learning for diagnosing Alzheimer's disease. In several diagnostic imaging classifications, Vision Transformers (ViT) recently outperformed CNN. Transformers allow attention to be drawn to all previously computed elements in a sequence so that they exhibit minimal inductive bias toward learning compression representations over time. A slow, naturally iterative stream tries to learn a specialized, compressed representation by grouping K time-step parts into a single representation decomposed into multiple vectors. With the proposed approach, we intend to achieve Transformer expressiveness while promoting improved representational structure and slow in-stream compression for ADNI dataset. For visual perception and sequential decision-making tasks, we demonstrate the advantages of the proposed technique in terms of improved sample efficiency and generalization performance over other competitive benchmarks. Accordingly, we propose a technique to identify dementia by combining 18F-Florbetaben PET scan with ViT. The results show that the proposed method can be successfully applied in the field of brain imaging and may offer a potential way to use the pre-trained model in dataintensive applications. Moreover, compared with most of the current studies, the proposed cross-domain transfer learning technique can achieve comparable classification performance. According to the experimental findings, the suggested model has an accuracy of 91.08% when applied to the ADNI database for AD/CN classification task. Then, to explain the findings, we offer an Explainable Artificial Intelligence paradigm using attention maps.

Increasingly common in the aging population, Alzheimer's disease (AD) is a neurodegenerative disorder. Early identification and care are the best ways to prevent AD. In several diagnostic imaging classifications and multiple groups of medical images, transformers (ViT) have recently shown classification results that are superior to CNN. ViT, which tracks the direct associations between images, may be more useful for brain image analysis than CNN since the brain is a complicated system with interconnected parts. Traditional ViT is unable to attend the target class efficiently due to iterative attention brought on by a large constant temperature factor and inductive bias. This work suggests shifted Patch Tokenization (SPT) and position encoding using CoordConv Position Encoding (CPE) to reduce the locality inductive bias of ViT. Moreover, we propose a gradient centralization technique with Adam optimizer for better and faster training. We demonstrate qualitatively how each strategy serves a more crucial setting and helps to identify Alzheimer's disease. The findings of the experiment show that using the suggested approach for distinguishing AD from HC yielded a classification accuracy of 92.30% with a sensitivity of 95.31% and a specificity of 91.45%, which made it the most cutting-edge technique in terms of diagnostic accuracy. These findings have shown the clinical relevance of the suggested approaches for identifying AD and have proven their efficacy.

Due to the ever increasing inter-dependency across a variety of diverse software and hardware components in ICT provisioning, supply chain vulnerabilities (SCVs) targeting such dependencies have evolved as a primary choice for malicious actors to initiate stealthy and complex cyber-attacks. The current modus operandi within the modern cyber threat spectrum is solely correlated with APTs that have shown to be quite prevalent across diversified attacks underpinning cyberwarfare, cyber terrorism and cybercrime in general. Thus, defense against such events is undoubtedly considered as a high priority on a global scale. Nonetheless, the integration of and dependence on third-party supply chain software and devices located at heterogeneous ICT ecosystems in parallel with the inability of current defense mechanisms to pinpoint nor consider enable a plethora of compromise entry points that consequently amplify APTs. Motivated by these challenges, this survey explores the state-of-the-art to (i) stratify and showcase the properties of supply chain-based APTs, (ii) elaborate on reported risks from such APTs, and, (iii) expand on existing defense methods as proposed until recently. The herein reported study aims to also relate academic research with industry practices having as a greater goal to expose this emerging issue and equip cybersecurity practitioners with the required knowledge for designing next generation APT defense mechanisms.

Business recommendations and proposals start with asking business questions, insights from answering those questions, recommendations from those insights and final proposals or recommendations for implementation. In this paper, we present an end-to-end solution framework called BIRD (Business Insights and Recommendations Developer) which implements Large Language Models (LLMs) as a major part of this business analysis cycle in developing business questions, extracting insights, and providing recommendations in an end-to-end automated process. This framework also allows user interaction at any step for additional context or commands.

The presented study introduces a semi-analytical circuit model to fully characterize a dielectric unit cell structure made by blocks instead of applying the effective medium theory. A fitting strategy os used to a obtain a better understanding of the properties of the unit cell. Then, the approach is used to develop a semi-analytical model, demonstrating a good agreement with the scattering parameters extracted from a full-wave simulator. By tuning the dielectric material, our model can describe the behaviour of unit cells with different permittivities and electrical sizes. Finally, as an application for the analyzed dielectric unit cells, transmitarrays to generate orbital angular momentum (OAM) waves are designed and 3D-printed. The measured results show good performance for different number of OAM modes at 31 GHz.

A document by Saleh Darzi. Click on the document to view its contents.

In the era of rapid digital transformation, the integration of Federated Learning (FL) with Edge Computing in 5G networks emerges as a pivotal innovation, offering a new paradigm for data processing and intelligence distribution. This paper delves into the core concepts of FL and its symbiotic relationship with Edge Computing within 5G infrastructures. We examine the unique challenges such as data privacy, computational resource management, and network reliability, alongside the dynamic opportunities that this integration presents. Through a comprehensive review of current research and developments, this study not only highlights the technological advancements but also sheds light on the socioeconomic impacts of FL in 5G Edge Computing. By offering perspectives from both industry and academia, the paper aims to chart a course for future research and implementation strategies, paving the way for a more connected and intelligent world.

This review explores the integration of conversational AI, particularly chatbots like ChatGPT, in problem-based teaching methodologies within diverse educational settings. The rapid advancement in AI technology offers unique opportunities for addressing the challenges faced by students from varied cultural and linguistic backgrounds. By focusing on problem-based learning (PBL) strategies enhanced by conversational AI, this paper examines how AI tools can facilitate personalized learning experiences, promote cultural understanding, and overcome language barriers. The review synthesizes current research findings, highlighting the effectiveness of AI in creating inclusive and adaptive learning environments. Special attention is given to the potential of these technologies in fostering critical thinking, collaboration, and problem-solving skills among students with diverse needs. The paper also addresses the ethical considerations and challenges in implementing AI in educational contexts.

This study aims to explore the efficacy of conversational AI technologies in assisting adults living with ADHD, diagnosed or undiagnosed, in coping with daily challenges and improving social skills. While ADHD is often associated with youth, many adults also struggle with its symptoms, which can include disorganization, impulsivity, and difficulties in maintaining relationships. This research will investigate how AI chatbots, through personalized interactions and behavioral modification strategies, can aid in managing these symptoms. The study will also consider factors to increase accessibility and engagement with these AI tools among adults with ADHD, addressing potential stigmas and technological barriers. Through a systematic review of current literature and case studies, this research aims to provide insights into the development of AI tools tailored for adult ADHD support, contributing to their social and functional well-being.

This research explores the potential of conversational artificial intelligence (AI) as a tool to support children and young adults with Attention Deficit Hyperactivity Disorder (ADHD), compulsive disorders, and other mental health challenges, particularly in educational settings. Recognizing the unique difficulties this demographic faces, the study aims to investigate how conversational AI can provide structured daily assistance, improve mental well-being, and enhance academic performance. The research draws on various interdisciplinary studies, including those focused on empathy-driven AI, AI in psychiatry, and chatbots in mental health. Through a systematic analysis of existing literature and case studies, this research endeavors to provide a comprehensive understanding of the effectiveness of conversational AI in supporting youth with ADHD and related disorders, offering insights into future developments in this field.

Weber-Maxwell electrodynamics is a modernized, compressed, cleansed and, in many respects, advantageous representation of classical electrodynamics that results from the Liénard-Wiechert potentials. In the non-relativistic domain, it is compatible with both Maxwell's electrodynamics and Weber electrodynamics. It is suitable for all electrical engineering tasks, ranging from electrical machines to radar and high-frequency technologies. Weber-Maxwell electrodynamics also simplifies access to quantum physics and other areas of modern physics, such as optics and atomic physics. Particular advantages of Weber-Maxwell electrodynamics are its simple and fast computability in computer calculations and, as it is based on point charges, in the simulation of plasmas. The latter is particularly important for fusion research. Moreover, Weber-Maxwell electrodynamics is also highly suited to academic and post-primary education, as it allows an easy comprehension of both magnetism and electromagnetic waves. Due to the novelty of Weber-Maxwell electrodynamics, there are currently no articles that summarize its most important aspects. The present article aims to achieve this.

The caching paradigm has been introduced to alleviate backhaul traffic load and to reduce latencies due to massive never ending increase in data traffic. To fully exploit the benefits offered by caching, unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication can be further utilized. In contrast to prior works, that strictly limits the content delivery routes up to two hops, we explore a multi-hop communications scenario, where the UAVs, the UEs, or both can relay the content to individual users. In this context, we formulate the problem for joint route selection and power allocation to minimize the overall system content delivery duration. First, motivated by the limitations of existing works, we consider the case where the nodes may transmit content simultaneously rather than sequentially and propose simple yet effective approach to allocate the transmission power. Second, we design a low-complexity greedy algorithm jointly handling route selection and power allocation. The simulation results demonstrate that the proposed greedy algorithm outperforms the benchmark algorithm by up to 56.98% in terms of content delivery duration while it achieves close-to-optimal performance.

In this paper, we propose a novel self-optimization framework that incorporates an automatic weight function for determining Handover Control Parameters (HCP) and utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm for target cell selection. The proposed algorithm estimates suitable HCP values automatically based on key factors such as Signal-to-Interference-plus-Noise Ratio (SINR), traffic load, and user equipment (UE)'s speed. Additionally, TOPSIS algorithm is employed to select the most suitable target cell based on three evaluation criteria: SINR, Reference Signal Received Power (RSRP), and load level. The performance of the proposed algorithm is evaluated using load level, throughput, and Radio Link Failure (RLF) as key performance indicators (KPIs). Extensive simulations are carried out to assess the performance of the proposed approach. The findings indicate that the proposed algorithm outperforms the existing solutions in terms of load level, throughput, and RLF.