International Journal of Emerging Engineering and Technology
type: channel
description: 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.
publisher: Swedish College of Engineering and Technology
language: en
publication name: International Journal of Emerging Engineering and Technology
issn: 2958-3764
items: Determination of Water Quality Index for Various Sources of Drinking Water in Tando Allahyar City
items: A Critical Analysis of Deepfake Detection Software: Emerging Challenges and Quality Assurance
items: Cybersecurity Challenges and Defense Mechanisms in IoT: A Comprehensive Analysis of Emerging Threats and Intelligent Solutions
items: Evaluation of Ecological Restoration for River Water Quality Improvement: A Modelling Approach
items: Synergistic Effects of Calcium Chloride and Polypropylene Fibre on Khanote Soil: Experimental and AI Modelling
items: AI in Digital and Mobile Forensics: A Thematic Literature Review
items: Experimental investigation of Slope Change Impact on Manning’s n in a tilting bed flume
items: XAI-Based Dynamic Cybersecurity Risk Management and Its Conceptual Architecture
items: AI-Driven Dynamic Risk Management in Cybersecurity
items: AI-Driven Cybersecurity for Satellite Systems: A Survey of Threats, Applications, Challenges and Future Directions
items: A Comprehensive Review on Network Security in AI-based Healthcare Systems
items: IoT-Based Zero-Trust Security Architecture: A Comprehensive Analysis
items: AI, Cybersecurity and Growing Ethical Dilemma: A Survey
items: AI-Based Network Intrusion Detection System for Enterprise and Cloud-based Infrastructures
items: Federated Learning and Artificial Intelligence in IoT: Security Challenges, Solutions, and Future Directions
items: Phishing Attacks in the Age of Artificial Intelligence: A Systematic Survey
items: Cybersecurity Challenges in Digital Forensics Types: Network Forensics, Cloud Forensics and Mobile Forensics
items: Analyze the Critical Factors of Security and Privacy Issues in IoT Domains
items: Programming Limitations and Challenges on Consumer Hardware GPU
items: AI-Based Clinical Decision Support System for Thyroid Nodule Classification in Ultrasound Imaging with Web-Based Implementation
items: Explainable Artificial Intelligence in Medical Image based Diagnosis: A Comprehensive Study
items: Enhancing the Photocatalytic Performance of Anatase TiO2 through Vanadium, Mangenese and Nitrogen Co-doping: A DFT Study
items: Electro-Mechanical Characteristic Analysis of the Surface Mounted Permanent Motor
items: Finite Element Analysis of the Surface Mounted Permanent Motor Without Stator Excitation
items: Artificial Intelligence–Driven Imaging Advances in Lung Fibrosis: A Comprehensive Review
items: Enhancing Facial Recognition Performance with Data Augmentation in Occluded Environments
items: A Review on Integration of Ultrasonic Vibration in Machining of Difficult-to-Cut Materials
items: Mini-review: Experimental Approaches for the Biomechanical Testing of Bone
items: AI-Powered Home Automation: A Simple and Smart Living Solution
items: Step-by-Step Installation of Solar Energy and Its Maintenance

Determination of Water Quality Index for Various Sources of Drinking Water in Tando Allahyar City
type: item
description: 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.
subject: Articles
  topic: section
subject: Water Quality index
  topic: keywords
subject: Tap water
  topic: keywords
subject: Ground Water
  topic: keywords
subject: Bottled Water
  topic: keywords
subject: Public Health
  topic: keywords
creator: Haji Darya Khan
creator: Ashfaque Ahmed Memon
creator: Aqib Munir
creator: Rana Waqas Ali
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 62
ending page: 67
doi: 10.57041/jrsh4c84

A Critical Analysis of Deepfake Detection Software: Emerging Challenges and Quality Assurance
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital Forensics
  topic: keywords
subject: Deepfake
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Digital Evidence
  topic: keywords
subject: Cybercrime
  topic: keywords
subject: Cyber Security
  topic: keywords
subject: Multimedia Forensics
  topic: keywords
subject: Software Quality Assurance
  topic: keywords
creator: Imran Nawaz Rana
creator: Irshad Ahmed Sumra
creator: Syeda Shan-e-Zahra
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 46
ending page: 54
doi: 10.57041/j5n19x40

Cybersecurity Challenges and Defense Mechanisms in IoT: A Comprehensive Analysis of Emerging Threats and Intelligent Solutions
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Intelligent Threat Detection
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: Intrusion Detection systems
  topic: keywords
subject: Lightweight Cryptography
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
creator: Humaira Ramzan
creator: Irshad Ahmed Sumra
creator: Syed Bilal Gillani
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 55
ending page: 61
doi: 10.57041/0nc42k53

Evaluation of Ecological Restoration for River Water Quality Improvement: A Modelling Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: river restoration
  topic: keywords
subject: water quality improvement
  topic: keywords
subject: bioremediation
  topic: keywords
subject: ecosystem resilience
  topic: keywords
subject: watershed management
  topic: keywords
creator: Abdul Munim Sahito
creator: Munesh Meghwar
creator: Umair Hussain Memon
creator: Ashfaque Ahmed Memon
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 40
ending page: 45
doi: 10.57041/v76kcd69

Synergistic Effects of Calcium Chloride and Polypropylene Fibre on Khanote Soil: Experimental and AI Modelling
type: item
description: 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.
subject: Articles
  topic: section
subject: soil stabilization
  topic: keywords
subject: polypropylene fibre
  topic: keywords
subject: CaCl2
  topic: keywords
subject: Artificial Neural Networks
  topic: keywords
subject: Predictive Modelling
  topic: keywords
creator: Abdul Munim Sahito
creator: Shafique Ahmed Korejo
creator: Muhammad Rehan Hakro
creator: Zaheer Ahmed Almani
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 22
ending page: 31
doi: 10.57041/ab2dfx24

AI in Digital and Mobile Forensics: A Thematic Literature Review
type: item
description: 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.
subject: Articles
  topic: section
subject: AI alignment
  topic: keywords
subject: Digital forensics
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Image classification
  topic: keywords
subject: large language models
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Mobile forensics
  topic: keywords
subject: Robustness
  topic: keywords
creator: Malik Ahsan Hayat
creator: Irshad Ahmed Sumra
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 52
ending page: 60
doi: 10.57041/3jhct150

Experimental investigation of Slope Change Impact on Manning’s n in a tilting bed flume
type: item
description: 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.
subject: Articles
  topic: section
subject: Manning’s n
  topic: keywords
subject: Tilting bed flume
  topic: keywords
subject: Bed slope impacts
  topic: keywords
creator: Muhammad Javid Khalid
creator: Muhammad Khurram Shahzad
creator: Muhammad Raza
creator: Muhammad Wajeeh Ul Hassan Raza
creator: Iqra Rafiq
creator: Sikandar Hanif
creator: Ayyaz Danish
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-29
publication date: 2026-06-29
starting page: 6
ending page: 15
doi: 10.57041/b4ze7t09

XAI-Based Dynamic Cybersecurity Risk Management and Its Conceptual Architecture
type: item
description: 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.
subject: Articles
  topic: section
subject: Vulnerability Prioritization
  topic: keywords
subject: CodeBERT
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Cyber Insurance
  topic: keywords
subject: Dynamic Risk Assessment
  topic: keywords
creator: Talha Hameed Almani
creator: Irshad Ahmed Sumra
creator: Bilal Gillani
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 16
ending page: 21
doi: 10.57041/jw60dr64

AI-Driven Dynamic Risk Management in Cybersecurity
type: item
description: 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.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Cyber Insurance
  topic: keywords
subject: Dynamic Risk Assessment
  topic: keywords
subject: Vulnerability Prioritization
  topic: keywords
creator: Abaidullah Butt
creator: Irshad Ahmed Sumra
creator: Muhammad Sohail Athar
creator: Malik Adnan
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 111
ending page: 117
doi: 10.57041/ttbrq395

AI-Driven Cybersecurity for Satellite Systems: A Survey of Threats, Applications, Challenges and Future Directions
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-driven cybersecurity
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: CatBoost
  topic: keywords
subject: cyber vulnerabilities
  topic: keywords
subject: Quantum cryptography
  topic: keywords
subject: Satellite cybersecurity
  topic: keywords
subject: space systems
  topic: keywords
creator: Sania Khan
creator: Irshad Ahmed Sumra
creator: Makhdoom Zain Ul Abdin
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 44
ending page: 51
doi: 10.57041/wf0vhc65

A Comprehensive Review on Network Security in AI-based Healthcare Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Adversarial Attacks
  topic: keywords
subject: Adversarial Machine Learning
  topic: keywords
subject: Artificial Intelligence in Healthcare
  topic: keywords
subject: Cybersecurity in Healthcare Systems
  topic: keywords
subject: Deep Learning Security
  topic: keywords
subject: Defense Mechanisms
  topic: keywords
subject: Evaluation stages
  topic: keywords
creator: Komal Shehzadi
creator: Irshad Ahmed Sumra
creator: Eeman Khokhar
creator: Benish Khalid
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 97
ending page: 103
doi: 10.57041/t8f22210

IoT-Based Zero-Trust Security Architecture: A Comprehensive Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Anomaly Detection
  topic: keywords
subject: Cross-Layer Co-Design
  topic: keywords
subject: Edge-Fog Computing
  topic: keywords
subject: Energy-Security Paradox
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Lightweight Cryptography
  topic: keywords
subject: Zero-Trust Architecture
  topic: keywords
creator: Erej Azeem
creator: Irshad Ahmed Sumra
creator: Mehrooj Shahid
creator: Abdul Sattar
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 77
ending page: 87
doi: 10.57041/bvepns17

AI, Cybersecurity and Growing Ethical Dilemma: A Survey
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: machine learning
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Ethical AI
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Cyber-Physical Systems
  topic: keywords
creator: Amna Iftikhar
creator: Irshad Ahmed Sumra
creator: Bilal Gillani
creator: Uneeb Raziq Khan
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 71
ending page: 76
doi: 10.57041/ccn1xy89

AI-Based Network Intrusion Detection System for Enterprise and Cloud-based Infrastructures
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Network Intrusion Detection System
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Technology Acceptance Model
  topic: keywords
creator: Malik Adnan
creator: Abdul Sattar
creator: Hamza Azeem
creator: Irshad Ahmed Sumra
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 104
ending page: 110
doi: 10.57041/pzctkn41

Federated Learning and Artificial Intelligence in IoT: Security Challenges, Solutions, and Future Directions
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Federated Learning (FL)
  topic: keywords
subject: Explainable Artificial Intelligence (XAI)
  topic: keywords
subject: IoT Security
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Cyber Attacks
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Intrusion Detection Systems (IDS)
  topic: keywords
creator: Bilal Gillani
creator: Irshad Ahmed Sumra
creator: Jahanzaib Sattar
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 32
ending page: 39
doi: 10.57041/e479ce67

Phishing Attacks in the Age of Artificial Intelligence: A Systematic Survey
type: item
description: 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.
subject: Articles
  topic: section
subject: Adversarial Attacks
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Phishing Detection
  topic: keywords
creator: Amara Nazir
creator: Irshad Ahmed Sumra
creator: Fahad Ali
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 61
ending page: 70
doi: 10.57041/gnjszy74

Cybersecurity Challenges in Digital Forensics Types: Network Forensics, Cloud Forensics and Mobile Forensics
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital Forensics,
  topic: keywords
subject: Network Forensics
  topic: keywords
subject: Cloud Forensics
  topic: keywords
subject: Mobile Forensics
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Digital Evidence
  topic: keywords
subject: Cybercrime
  topic: keywords
subject: Ransomware
  topic: keywords
subject: IoT Security
  topic: keywords
creator: Sidra Khan
creator: Irshad Ahmed Sumra
creator: Bilal Gillani
creator: Waqar Azeem
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 118
ending page: 125
doi: 10.57041/6ch2xv72

Analyze the Critical Factors of Security and Privacy Issues in IoT Domains
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things Security
  topic: keywords
subject: Connected Device Security
  topic: keywords
subject: Cyber-Physical System Security
  topic: keywords
subject: Embedded Device Security,
  topic: keywords
subject: Smart Device Security
  topic: keywords
subject: IoT Protection
  topic: keywords
subject: IoT Network Security
  topic: keywords
creator: Hamza Azeem
creator: Irshad Ahmed Sumra
creator: Syeda Shan e Zahra
creator: Malik Adnan
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 88
ending page: 96
doi: 10.57041/4s4fr974

Programming Limitations and Challenges on Consumer Hardware GPU
type: item
description: 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.
subject: Articles
  topic: section
subject: Distributed orchestration
  topic: keywords
subject: GPU computing
  topic: keywords
subject: heterogeneous architectures
  topic: keywords
subject: LLM serving
  topic: keywords
subject: preemptive multitasking
  topic: keywords
subject: soft errors
  topic: keywords
subject: thermal throttling
  topic: keywords
creator: Muhammad Ahmad
creator: Nabeel Akram
creator: Hira Arif
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 1
ending page: 5
doi: 10.57041/qjbmy594

AI-Based Clinical Decision Support System for Thyroid Nodule Classification in Ultrasound Imaging with Web-Based Implementation
type: item
description: 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.
subject: Articles
  topic: section
subject: Clinical Decision Support System
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: EfficientNet
  topic: keywords
subject: Thyroid Nodule Classification
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Ultrasound Imaging
  topic: keywords
creator: Hafsa Azam
creator: Alisha Ashraf
creator: Shahzad Hussain
creator: Ghazanfar Rehman
creator: Hammad Shahab
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 36
ending page: 43
doi: 10.57041/sqh1fr48

Explainable Artificial Intelligence in Medical Image based Diagnosis: A Comprehensive Study
type: item
description: 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.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Grad-CAM
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Clinical Diagnosis
  topic: keywords
creator: Adeela Hayat
creator: Zainab Azhar
creator: Amna Kosar
creator: Hafiz Burhan Ul Haq
creator: Sabir Abbas
creator: Rabia Younas
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 28
ending page: 35
doi: 10.57041/dygg4143

Enhancing the Photocatalytic Performance of Anatase TiO2 through Vanadium, Mangenese and Nitrogen Co-doping: A DFT Study
type: item
description: 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.
subject: Articles
  topic: section
subject: Anatase TiO2
  topic: keywords
subject: CASTEP Materials Studio
  topic: keywords
subject: Density Functional Theory (DFT)
  topic: keywords
subject: photocatalysis
  topic: keywords
subject: Fermi energy level
  topic: keywords
subject: photoelectric conversion
  topic: keywords
subject: electron-hole pair generation
  topic: keywords
creator: Nauman Ahmad
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 20
ending page: 27
doi: 10.57041/s2m6f335

Electro-Mechanical Characteristic Analysis of the Surface Mounted Permanent Motor
type: item
description: 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.
subject: Articles
  topic: section
subject: BLDC motor
  topic: keywords
subject: electrical machines
  topic: keywords
subject: electro-mechanical conversion
  topic: keywords
subject: finite element analysis
  topic: keywords
subject: permanent magnet (PM)
  topic: keywords
subject: SPM motor
  topic: keywords
creator: Qurban Ali Shah Syed
creator: Kamran Ahmed Samo
creator: Zahid Muhammad
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 15
ending page: 19
doi: 10.57041/jc0y9w02

Finite Element Analysis of the Surface Mounted Permanent Motor Without Stator Excitation
type: item
description: 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.
subject: Articles
  topic: section
subject: BLDC motor
  topic: keywords
subject: electrical machines
  topic: keywords
subject: electro-mechanical conversion
  topic: keywords
subject: finite element analysis
  topic: keywords
subject: no-load analysis
  topic: keywords
subject: permanent magnet (PM)
  topic: keywords
subject: SPM motor
  topic: keywords
creator: Qurban Ali Shah Syed
creator: Zahid Muhammad
creator: Kamran Ahmed Samo
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 10
ending page: 14
doi: 10.57041/4ssjeg12

Artificial Intelligence–Driven Imaging Advances in Lung Fibrosis: A Comprehensive Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Lung Fibrosis
  topic: keywords
subject: Idiopathic Pulmonary Fibrosis
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Quantitative Computed Tomography
  topic: keywords
subject: Post–COVID-19 Fibrosis
  topic: keywords
creator: Syeda Iqra Shakeel
creator: Sidra Aslam
creator: Hafiz Burhan Ul Haq
creator: Sabir Abbas
creator: Hafiz Muhammad Muneeb Akhtar
creator: Saleem Abbas
creator: Imran Khalid
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 1
ending page: 9
doi: 10.57041/vhsfkx67

Enhancing Facial Recognition Performance with Data Augmentation in Occluded Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Data Augmentation
  topic: keywords
subject: Occluded Environments
  topic: keywords
subject: Facial Recognition
  topic: keywords
subject: Model Generalization
  topic: keywords
subject: Robustness Analysis
  topic: keywords
creator: Afolabi Awodeyi
creator: Omolegho A. Ibok
creator: Jones U. Ekwemuka
creator: Omokaro Idama
creator: Edward Odesa
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-10
publication date: 2025-06-10
starting page: 27
ending page: 32
doi: 10.57041/pw10d472

A Review on Integration of Ultrasonic Vibration in Machining of Difficult-to-Cut Materials
type: item
description: 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.
subject: Articles
  topic: section
subject: Ultrasonic machining
  topic: keywords
subject: titanium alloys
  topic: keywords
subject: composite machining
  topic: keywords
subject: vibration-assisted cutting
  topic: keywords
subject: hybrid processes
  topic: keywords
subject: optimization
  topic: keywords
creator: Ravi Tyagi
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-10
publication date: 2025-06-10
starting page: 21
ending page: 26
doi: 10.57041/64jgxd81

Mini-review: Experimental Approaches for the Biomechanical Testing of Bone
type: item
description: 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.
subject: Articles
  topic: section
subject: Biomechanics
  topic: keywords
subject: bone
  topic: keywords
subject: nanoscale
  topic: keywords
subject: experimental
  topic: keywords
creator: Ioana-Cristina Bancila
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-10
publication date: 2025-06-10
starting page: 11
ending page: 20
doi: 10.57041/bnc97632

AI-Powered Home Automation: A Simple and Smart Living Solution
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: augmented reality
  topic: keywords
subject: home automation
  topic: keywords
subject: smart living
  topic: keywords
creator: Abdullah Islam
creator: Ahmad Saeed
creator: Adeel Hassan
creator: Zeeshan Shahid
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-14
publication date: 2025-06-14
starting page: 6
ending page: 10
doi: 10.57041/vj3bwb57

Step-by-Step Installation of Solar Energy and Its Maintenance
type: item
description: 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.
subject: Articles
  topic: section
subject: Solar system
  topic: keywords
subject: battery
  topic: keywords
subject: inverter
  topic: keywords
subject: mounting
  topic: keywords
subject: appliances
  topic: keywords
creator: Muhammad Haseeb Abid
creator: Wasif Shafiq
creator: Muhammad Dayem
creator: Hassan Riaz Khan
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-10
publication date: 2025-06-10
starting page: 1
ending page: 5
doi: 10.57041/7pbe1w24