International Journal of Emerging Engineering and Technology
International Journal of Emerging Engineering and Technology (IJEET) is a peer-reviewed, scientific and technical journal owned and published by the Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan. IJEET publishes high-quality original scientific articles dealing with the use of analytic and quantitative tools for the modelling, analysis, design and engineering management in the engineering and technology disciplines.
Swedish College of Engineering and Technology
Determination of Water Quality Index for Various Sources of Drinking Water in Tando Allahyar City
Water is vital to human existence, and recently, the demand for bottled water has increased for drinking purposes. However, people still use tap water and groundwater for different purposes due to their financial constraints. The examination of Tando Allahyar city's water quality is necessary due to the issue of people's access to clean drinking water. This research has determined the water quality of these three sources of drinking water, viz. Bottled water, tap water, and groundwater at Tando Allahyar were evaluated using the Water Quality Index (WQI) criteria. This research will increase public awareness. WQI was determined using various water quality parameters (physical, chemical, and biological), including pH, total dissolved solids, electrical conductivity, hardness, dissolved oxygen, total coliforms, and chloride. This research computes the WQI for all three water sources used in three selected areas of Tando Allahyar City. This research compares the WQI of various drinking water sources and proposes possible solutions to ensure safe, affordable drinking water. Three locations in Tando Allahyar provided tap and groundwater samples, which were gathered and evaluated in the lab. These locations include Prem Nagar, Qila Area, and Mujahid Colony, and the most commonly used Bottled Waters were collected in the same areas of the city. The WQI of tap water at three selected locations was within the good-quality range. The WQI of groundwater at two selected locations was in the poor-quality range; however, one location was in the good-quality range, though still on the borderline. The WQI of all seven bottled waters was in the excellent range. The study contributes to clean drinking water and raises awareness of the health risks posed by low-quality water.
Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
A Critical Analysis of Deepfake Detection Software: Emerging Challenges and Quality Assurance
Deepfakes have proven their identity in the field of digital forensics. The article explains how Deepfakes create highly realistic but synthetic videos, images, and audio clips, which create doubt about whether they are real or fake. This article identifies some important tools and technologies. In every field of life, such as entertainment, education, and content writing, deepfakes help impose a sense of authenticity. Digital forensics investigation agencies, law enforcement officials, and offices are also concerned about day-to-day social engineering attacks, identity theft, and misinformation. Software Quality Assurance (SQA) and testing features of the deepfake forensic systems, including accuracy, reliability, security, robustness, scalability, and explainability. The discussion covers testing challenges, including dataset validation, performance evaluation, adversarial testing, and real-world scenario testing, to enhance the reliability of AI-driven forensic solutions.
Cybersecurity Challenges and Defense Mechanisms in IoT: A Comprehensive Analysis of Emerging Threats and Intelligent Solutions
In the modern world, connected devices have revolutionized almost every aspect of life, from hospitals and factories to homes and city infrastructure. Today's large-scale IoT deployments have significantly expanded the attack surface, making it increasingly difficult for organizations to manage emerging cybersecurity risks. Legacy communication protocols, resource-constrained devices, and inadequate authentication mechanisms are frequently exploited for botnet recruitment, data theft, and service disruption. This paper provides a comprehensive overview of the IoT threat landscape and reviews defense mechanisms proposed to address these security challenges. It also examines the application of artificial intelligence (AI) and blockchain technologies in IoT security architectures while discussing emerging research directions, including federated learning, explainable AI, edge-native security, and post-quantum cryptography. The review concludes that no single security mechanism is sufficient to protect modern IoT ecosystems. Instead, a multilayered security strategy that integrates conventional security mechanisms with intelligent, adaptive technologies provides a more effective defense against evolving cyber threats.
Evaluation of Ecological Restoration for River Water Quality Improvement: A Modelling Approach
River ecosystems play vital roles in water provision, flood mitigation, and biodiversity conservation, yet face increasing degradation from human activities. This review systematically evaluates modern ecological restoration approaches, including riparian buffer rehabilitation, in-stream habitat improvement, and bioremediation technologies for their effectiveness in improving water quality. Through comprehensive analysis of field implementations and monitoring data, we demonstrate how restoration strategies significantly elevate dissolved oxygen levels (15-25% increase), reduce nutrient concentrations (30-50% decrease in nitrogen/phosphorus), and improve biodiversity indices (20-40% species richness improvement). Emerging techniques like constructed wetlands and bio-floating islands show particular promise, achieving 60-75% pollutant removal efficiency while providing additional ecological benefits. The study reveals three critical success factors: 1) integrated application of physical-biological-chemical methods, 2) community-engaged adaptive management, and 3) watershed-scale implementation. The findings establish that properly designed restoration not only reverses water quality degradation but also increases ecosystem resilience to future stressors. This work provides a science-based framework for selecting and optimizing restoration strategies tailored to specific riverine conditions and pollution contests.
Synergistic Effects of Calcium Chloride and Polypropylene Fibre on Khanote Soil: Experimental and AI Modelling
This study investigates the stabilization of marginal Khanote soil (A-1) using Calcium Chloride and Polypropylene Fibre, coupled with Artificial Neural Network predictive modelling. Laboratory evaluations involved Modified Proctor and Unconfined Compressive Strength tests across varying additive dosages. Results demonstrate that 1% CaCl2 significantly improves compaction, increasing maximum dry density from 1960 kg/m3 to 2130 kg/m3 through hygroscopic densification. Conversely, polypropylene fibre acts as a mechanical bridge, with an optimal 0.5% dosage yielding a peak UCS of 4.12 MPa, effectively transitioning the soil from brittle to ductile failure. A synergistic mix of 1% CaCl2 and 0.5% polypropylene fibre produced a robust UCS of 3.95 MPa, balancing chemical density with mechanical elasticity. To optimize mix designs, a multi-layer perception ANN is developed, achieving an overall correlation coefficient (R2) of 0.988 and low error metrics (RMSE = 0.079 MPa). The AI model successfully captured non-linear behaviours, including strength degradation associated with fibre agglomeration at 1.5% fibre content. This research provides a high-precision, data-driven framework for soil stabilization, offering civil engineers a sustainable tool for infrastructural development in arid regions by minimizing trial-and-error laboratory cycles while ensuring superior structural integrity. The findings validate AI as a transformative asset for modern geotechnical engineering.
AI in Digital and Mobile Forensics: A Thematic Literature Review
The vast amount of information that modern devices produce presents a big challenge for investigators— one that Artificial Intelligence (AI) and Machine Learning (ML) seem perfectly suited to solve. Indeed, AI and ML have been used more and more in digital forensics over the past decade. To better understand this trend, we conducted a thematic literature review of 29 papers published between 2010 and 2025. These papers fell into seven categories: core ML techniques; what practitioners think; image/multimedia classification; explainable AI; robustness/adversarial issues; large language models (LLMs); and governance/legal matters. We also examined three overarching issues: how accurate these tools really are; whether they can be trusted (a question that goes to their legal admissibility); and whether they produce results that humans can understand. Based on our findings, we identified three urgent needs for the field: standardized operating procedures; public benchmarks; and systems that are not only technically sound but also legally admissible and human-friendly.
Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
Experimental investigation of Slope Change Impact on Manning’s n in a tilting bed flume
This paper presents the findings of laboratory investigations on a tilting bed flume to determine the impact of bed slope on Manning’s roughness coefficient, n (Manning’s n). Correct estimation of Manning’s n is crucial in the relatively accurate approximations of velocity and discharge. While various studies have examined factors affecting Manning’s n, limited research has assessed the direct impact of bed slope. In this study, a tilting bed flume with a uniform graded 12.5 mm gravel bed was used to investigate the variation of water depth along the flume length at various slopes. Using the discharge computed by the volumetric method at the collection container and water depth measured at three points within the flume, the cross-sectional area, hydraulic radius, velocity, and Manning’s n were computed at three different slopes: 0.04, 0.09 and 0.13. It was found that the average water depth, cross-sectional area, and hydraulic radius decreased, whereas the velocity and discharge increased as the bed slope increased. The results revealed that Manning’s n increases by 50% as the slope increases from 0.04 to 0.09. However, the value of Manning’s n decreased when further increasing the slope to 0.13. These findings revealed conclusively that Manning’s n is not independent of the bed slope. Therefore, a factor of slope should be incorporated alongside other aspects to determine a more accurate value of Manning’s n.
XAI-Based Dynamic Cybersecurity Risk Management and Its Conceptual Architecture
Managing cybersecurity risk is a basic requirement for preserving systemic resilience in an increasingly unstable digital world, regardless of an organization's size or operational type. There is a noticeable knowledge asymmetry in the corporate cyber insurance underwriting market due to the inability of traditional qualitative or static risk assessment techniques to identify evolving threat vectors. To provide real-time, dynamic cybersecurity threat quantification, this survey offers a thorough analysis of cutting-edge architectures that combine Explainable Artificial Intelligence (XAI) with bimodal Large Language Models (LLMs). This study develops an objective operational framework for risk transparency. It carefully evaluates multi-col-linear feature correlation measures, interpretive mathematical frameworks like SHAP (Shapley Additive explanations), and natural language and source-code tokenization engines like CodeBERT. Additionally, we investigate how these automated approaches directly link control verifications to the National Institute of Standards and Technology (NIST) Special Publication 800-53 protocol while balancing intricate technological vulnerability telemetries with conventional regulatory frameworks. In the end, this survey creates a structured taxonomy of current literature, describes structural mapping procedures, pinpoints systemic research gaps concerning the discoverability of open-source assets, and delineates crucial future paths for constructing transparent, auditable, and insurable corporate technical infrastructures.
AI-Driven Dynamic Risk Management in Cybersecurity
Because of the growing technological sophistication of companies, the threat of attacks grows, and static risk assessments can no longer meet the requirements. AI-powered systems can provide real-time detection capabilities, but the "black box" nature of the technology makes it difficult to ensure their adoption and to justify their financial cost. This brings the need for transparency and explanations of how AI works in cybersecurity, so that organizations can better comprehend, validate, and rely on AI's intelligent decision-making. The survey assesses recent progress in explaining AI (XAI) in dynamic cybersecurity risk management in critical infrastructure systems such as 5G and banking. We examine techniques from LLM-based vulnerability prioritization to hybrid cascading risk models. This paper outlines how recent frameworks have leveraged multiple datasets, such as EPSS and CISA KEV, to enhance remediation efficiency by up to 95%. Unlike existing surveys, this paper provides a unified review of explainable AI, dynamic cybersecurity risk management, governance, and cyber insurance. It also highlights existing research on these fields, explores practical implementation issues like computational needs, data protection, and model generalization, and proposes future research avenues to create not only transparent but also trusted frameworks for mitigating cyberattacks using AI techniques. The analysis further demonstrates that XAI tools such as SHAP and LIME are essential for the financial health of cybersecurity. Lastly, attention must be given to changing the auditable logic to make technical evidence measurable and insurable.
AI-Driven Cybersecurity for Satellite Systems: A Survey of Threats, Applications, Challenges and Future Directions
Satellite systems have become increasingly important for communication, navigation, defence, emergency management, Earth observation, finance, and other critical infrastructure services. These systems are increasingly connected to ground stations, cloud platforms, Commercial Off-the-Shelf (COTS) systems, open-source software, and Artificial Intelligence (AI) and autonomous systems, thereby increasing their cyberattack surface. This survey explores the threats, applications, challenges, and future directions of AI in satellite-based cybersecurity in this survey. It highlights two recent studies, one of which combines Quantum Key Distribution (QKD) with CatBoost Machine Learning (ML) for secure satellite communications, and the other of which is a taxonomy of cyber vulnerabilities, spanning legacy, operational, and AI-enabled space systems. The review concludes that AI can be used for anomaly detection, threat classification, risk prioritisation and decision support, and quantum cryptography can enhance key exchange. To ensure future progress, there is a need for explainable AI and resilient security frameworks.
A Comprehensive Review on Network Security in AI-based Healthcare Systems
Artificial Intelligence (AI) has revolutionized healthcare with automated diagnosis and predictive analytics. However, the application of AI in healthcare systems introduces serious cybersecurity concerns, particularly concerning data integrity and model robustness. The robustness of Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest models to adversarial attacks such as the Fast Gradient Sign Method (FGSM) and data poisoning is tested in this study. We take a hybrid experimental setting to estimate the performance of the model under clean, attacked and defense scenarios. The results show that CNN is highly vulnerable to adversarial attacks, while Random Forest is relatively more stable. While defence mechanisms such as adversarial training and knowledge distillation can improve the model’s performance, they cannot completely eliminate cybersecurity threats. The study emphasises the need for multi-layered cybersecurity frameworks. Such frameworks are necessary to guarantee the safety, privacy and reliability of AI-driven healthcare systems.
IoT-Based Zero-Trust Security Architecture: A Comprehensive Analysis
Traditional perimeter-based security models have become ineffective in the face of the explosive growth in the number of Internet of Things (IoT) endpoints deployed at critical infrastructures. To cope with this weakness, network engineering is moving toward Zero-Trust Architecture (ZTA) frameworks, based on the principle of "never trust, always verify". But enterprise zero-trust models deployed directly in deeply embedded, resource-constrained nodes create a critical operational conflict: the Energy-Security Paradox. The survey paper offers an in-depth and multidimensional analysis of scientific publications from 2024 to 2026 to chart the convergence of cutting-edge IoT Zero-Trust deployments under stringent hardware and computational constraints. Using a Systematic Literature Review (SLR) methodology, a technical taxonomy was developed to sort eligible studies into a structured classification of studies in the three operational layers: physical hardware layers, trust anchors, lightweight cryptography (LWC), and decentralized behavioural intelligence. Silicon-derived fingerprints are still environmentally sensitive, standalone LWC ciphers do not have extensive cross-layer protocol integration and decentralized threat detection models are still highly susceptible to adversarial manipulation and poisoning attacks. This survey paper outlines some of the most important open challenges relevant to the next decade of research, including Byzantine-resilient aggregation frameworks.
AI, Cybersecurity and Growing Ethical Dilemma: A Survey
This survey paper examines the work of various researchers. It states that cybersecurity threats are increasing rapidly and becoming harder to prevent. A new subject, ‘Artificial Intelligence,' has entered this space as a means for specialists to combat evolving cybersecurity threats and attacks. Utilising techniques such as threat identification and automated responses can help organisations protect sensitive and confidential data, ensuring security across corporations in a highly unsafe environment. This paper discusses the ethical use of AI in cybersecurity and demonstrates the need to ensure that new technologies are developed and used fairly, ethically, and responsibly.
AI-Based Network Intrusion Detection System for Enterprise and Cloud-based Infrastructures
The proliferation of enterprise networks, distributed cloud structures, and the Internet of Things (IoT) has increased the dangers and vulnerabilities of digital infrastructure to sophisticated cyberattacks. Traditional Network Intrusion Detection Systems (NIDS) rely solely on static signature matching, which can't detect new zero-day attacks, and standalone systems that use anomaly detection techniques suffer from high false-positive rates that can overload Security Operations Centres (SOCs). One of the main problems is accurately identifying threats while maintaining the detection system's adaptability and scalability, which challenge is increasingly addressed by leveraging Artificial Intelligence (AI) and deep learning. The literature review and synthesis are systematic and conducted using peer-reviewed literature on the topic from 2018 to 2026, following an explicit methodology aligned with the PRISMA guidelines. The takeaway messages are that cost-sensitive and deep hybrid architectures outperform both the cost-sensitive and cost-ignorant benchmarks, but their effectiveness critically depends on model explainability, evaluation rigour, and threshold tuning. Lastly, this survey summarizes some of the biggest challenges and future research directions in explainable AI (XAI), federated learning, as well as lightweight and resource-aware modeling for network security.
Federated Learning and Artificial Intelligence in IoT: Security Challenges, Solutions, and Future Directions
There has been a great proliferation of the Internet of Things in different areas, ranging from healthcare, smart cities, industry, transport to agriculture, among others, and as a result of which an immense volume of distributed data is generated. The problem with traditional centralized machine learning methods is in the need to transfer sensitive data to centralized servers, which leads to additional risks of privacy breaches, communication overhead, and failure of single point. As a way of solving mentioned challenges, there appears the federated learning framework, which allows making collaborative model training without data transfer thus increasing the privacy of the system and saving on communications.
Explainable Artificial Intelligence (XAI)
Phishing Attacks in the Age of Artificial Intelligence: A Systematic Survey
One of the most dangerous frauds from today's techno-psychological era is phishing that plays on the vulnerabilities of technology and human psychology through email, SMS, telephone or social media and other new channels. Phishing attacks have evolved significantly with the advent of Artificial Intelligence, changing their nature, scope, and stealthiness, and continuing the arms race with defenders. The results reveal a number of points that need to be addressed, such as the growing prevalence of detections, the continued vulnerabilities within the ML systems and the necessity for multi-layered defense mechanisms that combine automated AI answers with controlled, human monitoring. Critical gaps include explainable AI, non-email vector coverage, and adversarial robustness benchmarks.
Cybersecurity Challenges in Digital Forensics Types: Network Forensics, Cloud Forensics and Mobile Forensics
The rapid growth of information and communication technologies (ICTs) has increased the risk of cybercrime, data breaches, ransomware attacks, and other cybersecurity threats. Digital forensics plays a crucial role in investigating cyber incidents by collecting, preserving, analyzing, and presenting digital evidence from various digital environments. This review paper discusses the concept of digital forensics and its major types, including network, cloud, and mobile forensics. It highlights the importance of these forensic domains in identifying cyber threats, supporting investigations, and enhancing cybersecurity. The paper also examines key challenges related to digital investigations, ransomware attacks, Internet of Things (IoT) environments, and emerging technologies. Furthermore, it reviews recent research developments and future directions aimed at improving forensic capabilities, evidence management, and cybersecurity defenses in modern digital systems.
Analyze the Critical Factors of Security and Privacy Issues in IoT Domains
The Internet of Things is all around us, influencing our personal as well as business lives. IoT devices communicate personal data to provide some modern luxuries, raising privacy questions that affect almost every aspect of modern life. This survey will examine the security of IoT, particularly in Smart Homes, Industrial Internet of Things, and Internet of Medical Things, and will propose that solutions to some challenges in one area could be applicable to solving challenges in other areas due to the heterogeneity of IoT. The challenges IoT devices present in terms of privacy and security have a specific relevance in a variety of industries: they could make our households insecure (smart homes), they could disrupt a whole industry (industrial IoT), or they could be physically damaging to us in a medical context (internet of medical things). In general, the rush to put IoT hardware, software, and devices into use is prioritised over consideration of whether they are actually secure. The development of IoT is so fast-paced that it sometimes creates security vulnerabilities, which may in turn compromise personal information or information held by an organisation or business. If IoT is used in a medical context, these vulnerabilities could be detrimental, resulting in injury/physical harm to people who were expecting services, rather than disruptions.
Programming Limitations and Challenges on Consumer Hardware GPU
Traditionally, high-performance computing was dominated by processors specifically designed to execute highly efficient mathematical calculations. But there are major architectural, thermal and software challenges involved in using consumer-grade and edge GPU hardware. We discuss the structural shortcomings in GPU programming, including a lack of fine-grained preemptive multitasking, thermal throttling under tight power limits, and memory vulnerabilities in the absence of enterprise-grade Error Checking and Correction on graphics cards. We also examine new software bottlenecks, specifically in LLM serving, and address challenges in distributed orchestration. In this work, we review existing frameworks such as FLEP, Phoenix and FlowPrefill to exemplify the need for full-stack solutions that are holistic in scope (encompassing hardware-level pre-emption via resource-efficient flow control, spatial sharing among diverse workloads, as well as dynamic scheduling) if we want to navigate around limitations posed by emerging heterogeneous compute architectures.
AI-Based Clinical Decision Support System for Thyroid Nodule Classification in Ultrasound Imaging with Web-Based Implementation
Thyroid nodules are a frequent clinical phenomenon and proper classification into the normal, benign, and malignant types is required to diagnose and plan treatment at an early phase. One of the most common imaging techniques is ultrasound, which is non-invasive, economical, but its interpretation is subjective, and it requires the expertise of the radiologist. In this study, a novel intelligent artificial intelligence (AI) system of diagnostic thyroid nodules through ultrasound imaging is offered. An efficient preprocessing pipeline, comprising of image resizing, intensity normalization, CLAHE-based contrast enhancement, median filtering, and data augmentation, was used to improve image quality and the performance of the model. A variety of deep learning models were tested (a baseline convolutional neural network (CNN)) and various transfer learning architectures. The CNN baseline obtained 76% accuracy and transfer learning models greatly enhanced performance. VGG16 and MobileNetV2 were capable of 84% and 88% accuracy, respectively, but ResNet50 scored 91 %. EfficientNet achieved the highest performance of 94% accuracy, high recall, precision, and F1-score. The findings show that transfer learning was effective in classifying thyroid ultrasound and the proposed system has potential to be an effective clinical decision-support tool in enhancing diagnostic accuracy and early diagnosis of malignant cases.
Explainable Artificial Intelligence in Medical Image based Diagnosis: A Comprehensive Study
Artificial intelligence (AI) has made remarkable contributions to medical imaging, enhancing the accuracy of the diagnosis and its efficiency; the challenge to the adoption in clinical settings has been the lack of clarity of the deep learning models. Explainable Artificial Intelligence (XAI) is a solution to this dilemma, as it provides easy-to-understand and understandable information about the way the model arrived at its decisions. The review summarizes the latest advances in XAI in the radiology, ophthalmology, pathology, neurology, and oncology domains as well as the conventional machine learning attribution algorithms; saliency-based deep learning algorithms, including Grad-CAM, SHAP, and LIME; and multimodal models. Its main clinical uses are cancer detection, classification of Alzheimer's disease, COVID-19 diagnosis, and evaluation of retinal disease. There are both quantitative (fidelity, stability, and localization) and qualitative (interpretability and radiologist trust) evaluation strategies. Although there is significant improvement, there exist dataset quality issues, interpretability-accuracy trade-offs, and generalizability issues across clinical settings. Furthermore, a comparative study with the available literature is done in a systematically organized way the evaluation parameters are clearly developed to evaluate objectively the strengths of our approach. This comparative parameter-based parameter shows the advances of our framework with respect to generalizability, explainability quality, and clinical relevance. Our study makes a more effective justification of its usefulness than the previous methods since the comparison is based on quantifiable measures.
Enhancing the Photocatalytic Performance of Anatase TiO2 through Vanadium, Mangenese and Nitrogen Co-doping: A DFT Study
TiO2 is a well-known photocatalyst with several potential applications. But because of its wider band gap, its optical activity is restricted to the UV region of the electromagnetic spectrum. These peak limits the photocatalytic activity of the specimen. To boost the photocatalytic activity of TiO2, it is necessary to shift its absorption peak from the ultraviolet to the visible range by reducing its bandgap. Doping of several metals and non-metals is one of the strategies adopted to decrease the bandgap of TiO2. This research article provides an overview of the effects of co-doping with Vanadium, Manganese, and Nitrogen on the structural and optoelectronic properties of anatase TiO2 using the framework of Density Functional Theory (DFT). V–N/TiO2, Mn–N/TiO2, and Mn–V/TiO2 were prepared, in which Mn and V were completely replaced at the i site, while N was at the O site. It was predicted that (V–N) doping in TiO2 decrease the bandgap from 2.13eV to 1.66eV. This decrease occurred due to the newly generated electronic states, i.e. V–3d and N–2p, below the conduction band minimum and the valence band maximum, respectively. (Mn– N) and (Mn–V) co-doping in TiO2 decrease the value of bandgap to 1.36eV and 1.22eV respectively. In these two models, the Fermi surface moved above the conduction band minimum, and thus, these models acted as N–type semiconductor. Co-doping also enhanced the optical performance of the specimen. It was observed that optical absorption increases as the model's bandgap energy decreases. The most enhanced optical absorption performance was predicted for Mn–V/TiO2 due to the effective electron-hole pair recombination.
Electro-Mechanical Characteristic Analysis of the Surface Mounted Permanent Motor
This paper presents a detailed electro-mechanical performance analysis of the three-phase brushless DC (BLDC) surface-mounted permanent magnet (SPM) motor. The BLDC SPM motor operates at the rated load and rated constant rated speed conditions. The electro-mechanical characteristics of the BLDC SPM motor include the air-gap magnetic flux distribution, mechanical torque, torque ripple, mechanical output power and electro-mechanical losses. The two-dimensional (2D) finite element analysis (FEA) is utilized to model and numerical analysis of the BLDC SPM motor.
Finite Element Analysis of the Surface Mounted Permanent Motor Without Stator Excitation
This paper presents an electromagnetic analysis of the brushless DC (BLDC) surface-mounted permanent magnet (SPM) motor using 2D finite element analysis (FEA). The no-load characteristics of the three-phase BLDC SPM motor are numerically computed, and the results are analysed in detail. The back EMF waveform of the three-phase BLDC SPM motor is computed when stator windings are not supplied with current, and its harmonic analysis is carried out using the fast Fourier transform (FFT). The cogging torque is computed without stator current excitation. The no-load electromagnetic results are discussed to analyse their influence on the performance of the three-phase BLDC SPM motor.
Artificial Intelligence–Driven Imaging Advances in Lung Fibrosis: A Comprehensive Review
Lung fibrosis is a chronic and progressive illness where there is pathologic tissue scarring, which affects lung architecture and respiratory organ functioning. It is caused by several factors, including idiopathic pulmonary fibrosis (IPF), radiation-related injury, tumour-related fibrosis, and post-COVID-19 complications, and all of them are associated with similar pathophysiology. This review investigates and summarises studies published from 2015 to 2025 on biological processes, clinical symptoms, and technological innovations in the diagnosis and monitoring of lung fibrosis. It emphasises the way that artificial intelligence (AI) and deep learning (DL) models, such as quantitative computed tomography (CT) and convolutional neural networks (CNNs), have enhanced the process of early detection, disease classification, and medical progression forecasting. The computational models, such as the agent-based and Monte Carlo simulations, which are used to study fibrotic dynamics, are also discussed in the review. In general, the combination of molecular knowledge, imaging, and AI-based systems can be considered a major next step in the creation of personalized diagnoses and better treatment outcomes in chronic fibrotic lung diseases.
Enhancing Facial Recognition Performance with Data Augmentation in Occluded Environments
Facial recognition systems frequently face challenges in accurately identifying individuals when critical facial features are obscured by occlusions such as masks, sunglasses, or scarves. These scenarios degrade the reliability of recognition models, especially in real-world environments. Data augmentation has emerged as a powerful strategy to improve model generalisation by artificially increasing dataset diversity and simulating occlusion-rich conditions. This study investigates the role of augmentation techniques, which include rotation, mirroring, zooming, and brightness adjustment, in enhancing recognition accuracy under partial and full occlusions. Experimental evaluation demonstrates that models trained with data augmentation achieve notable improvements over non-augmented baselines. For instance, average recognition accuracy improved from 72.4% to 86.3% under face mask occlusions, from 70.1% to 84.7% under sunglasses occlusions, and from 68.9% to 82.5% under scarf occlusions. When augmentation was combined with illumination normalisation, further gains were observed, with overall accuracy reaching 88.9% and F1-scores exceeding 87%. These results confirm that data augmentation substantially improves resilience against occlusions, while combined augmentation pipelines provide additional robustness in variable lighting and pose conditions. The findings highlight data augmentation as a foundational strategy for developing more resilient facial recognition systems. This work advances recognition performance in occlusion-heavy environments, with implications for applications in security, surveillance, and identity verification.
A Review on Integration of Ultrasonic Vibration in Machining of Difficult-to-Cut Materials
This review compiles and analyzes forty-five recent studies exploring ultrasonic-assisted turning, milling, drilling, grinding, and hybrid techniques across diverse industries, including aerospace, automotive, biomedical, and energy. Each study is evaluated based on material type, machine setup, input parameters, and key output responses such as tool wear, surface integrity, delamination, and cutting temperature. The review highlights substantial improvements in machinability metrics but also reveals critical limitations, including scalability issues, lack of adaptive control, limited economic assessments, and insufficient long-term tool wear analysis. The paper concludes with research gaps in real-time adaptive control systems in ultrasonic vibration-assisted machining and proposes future directions for integrating intelligent control systems, AI-driven optimization, and sustainable UVAM solutions compatible with Industry 4.0 environments.
Mini-review: Experimental Approaches for the Biomechanical Testing of Bone
Mechanical testing of bone biomechanics is crucial for assessing bone strength, quality, and fracture risk. This mini-review summarizes recent advances from nanoindentation to whole-bone compression, highlighting techniques such as in situ micro-CT with digital volume correlation, finite element modelling from clinical imaging, and biomechanical sensors. Despite progress, challenges in standardization, scalability, and clinical translation persist. Future directions focus on multiscale testing, machine learning integration, and interdisciplinary approaches to improve patient-specific diagnosis and treatment. Combining experimental, computational, and clinical methods is essential for advancing bone health research.
AI-Powered Home Automation: A Simple and Smart Living Solution
This paper explains a smart home system that uses artificial intelligence (AI) to make life easier, safer, and more comfortable. The system uses voice commands, face recognition, and augmented reality (AR) so people can control home devices in simple ways. It also processes data quickly and securely. This kind of smart home can save energy, improve safety, and provide a better experience for users. There are many systems developed previously, but everyone has its own merits and demerits. The proposed system is power efficient, cost-effective and computationally intensive. The deployment of the system is very simple, and the adaptability to the new features is very flexible. The adaptability of the proposed system makes it most prominent and is gaining attention from the community.
Step-by-Step Installation of Solar Energy and Its Maintenance
The study reviews how solar panels are a green energy solution and discusses the procedure for installing solar panels from beginning to end. As costs continue to increase, as environmental issues are raised and with geopolitical issues also involved, solar energy use has risen as an alternative to regular energy sources. The analysis points out that solar energy can reduce arguments over energy supplies, make people less dependent on rare fossil fuels and give zero emissions of gases that impact climate change. While installing a solar system, the solar panels, inverters, batteries and all the needed cables are talked about in detail, as are their individual uses. Also, for the most efficient use of energy, selecting devices that protect the environment and cooperate with solar systems is emphasized. This research explores how solar can help meet energy needs sustainably and without harming the environment, by closely inspecting each stage of installing a solar system.