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
Dates Ripen State Identification Deep Learning Technique
One of the primary factors is that conventional approaches are expensive and time-consuming. An automatic system for classifying the maturity of palm dates was created in this suggested system to identify the various phases of date fruit ripeness quickly and accurately. The three stages of our system's operation are the one-stage deep learning model (You Only Look Once Yolov4) algorithm used to identify palm dates in a video frame, the centroid tracking algorithm used to keep track of each vehicle within a defined area of interest, and palm date ripen state detection algorithm. While the centroid tracking technique can effectively follow any moving item, the deep learning model (You Only Look Once Yolov4) approach is particularly accurate at detecting objects. A test using a few traffic movies demonstrates that our suggested approach can detect and identify any ripen state detection of dates in various lighting and weather scenarios. The technology is straightforward to use and put into place.
Copyright (c) 2022 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
Essential Job Placement and Skill Learning for HBL Asset Management
We are offering a stable position in HBL assets. Through the admin panel, the jobs would be announced on the application, and they would then hire an employee or employees. Everything will be relating on hiring, whether it will be training or internships, would be listed in there. A regular person could easily use the application. First, he or she would sign up or log in using their Google account or another email address. Then, next step is to apply for a job, they would simply click on the advertisement for that job that was posted on the application. Alternatively, they could search for the jobs they need. If the position he or she is looking for is found, they can just click on it and uploads their resume in PDF format. If the Bank deems him qualified, they will set up an interview with him by getting in touch with him via the email or phone number provided in the resume. Currently, the only way to apply for a job was to keep sending emails to the HR department.
The Behaviour of Hinges in Buildings Over Various Monitored Displacement - Pushover Analysis
Before, most of the existing multi-storey building was designed only for gravity load. Later only linear analysis considering the design was adopted. But linear analysis results with reduced displacement and greater base force were obtained only up to the yield point. We must adopt nonlinear analysis, such as pushover analysis, to predict results beyond the yield point. In the case of pushover analysis, results beyond the yield point are also obtained. The nonlinear analysis gives a clear picture of different failure stages of the building before yield and also at collapse. The results of base force corresponding to displacement with hinges formed at various failure states such as A-B, B-IO, IO-LS, and LS-CP state is obtained. It also gives results of Demand. In the present problem, a 10 storey 5x5 bay structure is nominated. The building is subjected to pushover analysis and pushed for various monitored displacements starting from 0.1m to 4.0m. The formation of hinges at various failure states notes the variations occurring in the performance of the building. Observing the outcome gives an idea to adopt retrofitting technique or not.
The Development of IOT Based Virtual Doctor Robot
Recently, COVID-19 affected millions of people worldwide, so healthcare and routine checkups have become impossible for hospital doctors. The doctor Robot is comfortable for hospitals and has become necessary for all hospitals in emergency and viral infectious diseases. In Virtual Doctor Robot Project, a 2 to 3 feet Robot will be designed by using Arduino. We will design it in 5 steps. First, the line following the Robot base will be designed, and we will ensure the robot moves towards 3 to 4 virtual beds. An Android App will be designed by which the user can command the robot to follow which line and to reach which virtual bed station. Then in the second step, we will design the robot's frame. The third step is to design and code the sensors to detect if anything blocks the robot's path, and then the buzzer will turn on. Forth step is to design, code and manufacture the medical checkup instruments, which will be directly inserted into the robot. The doctor will command the robot what medical Report he wants by using Android App. The fifth step is to design and code RFID for Reports to scan patient cards to verify patients. The doctor will use a panel mainly based on IOT to manage the mechanism. Overall, the base has other functions and the battery status, which reacts to the battery charging prompt in good time.
Modern Trends in Manufacturing of Medium Voltage Switchgear
Switchgear is regarded as crucial equipment innetworks that distribute electric power for protection. This is because it's essential to keep an eye on the Switchgear's performance and condition and to carry out the necessary corrective maintenance on any equipment that might be causing issues. A single incident could harm numerous customers, and operational staff would be seriously risked. Before employing outages to shut down the system, many factors must be considered because they may increase maintenance costs and impair the users' access to electricity. Therefore, accurate switchgear status evaluation interpretation is essential for the early detection of potential failures. The production of MV switchgear is discussed in this study.
Lean Manufacturing Practices with context to Senior Management: A Study of Manufacturing Companies of Pakistan
Organizations are nowadays focusing onutilizing the latest technologies to reduce the overall manufacturing costs without compromising on the product quality, and getting a competitive advantage. Lean principles are hence the mostly used manufacturing principles to get this objective of increased productivity by lowering the waste. Lean management and implementation is surely a complete team work as we can say, but is still majorly dependent on the senior management of any organization. This study goes with the purpose of investigating the criticality of the role of the senior or executive level management in the successful implementation of the lean manufacturing principles and how much difference it’s going to show with the current scenarios. The data has been collected by sending a questionnaire which has 8 statements, targeting on the critical success factors, to the top management of 50 automotive manufacturing organizations of Karachi. The lean manufacturing trend and the involvement of the senior management towards its implementation is being identified after critically examining the received feedbacks.
Proprietress of Intelligent Political Systems
Artificial Intelligence is becoming most prominent aspect of technology in Finance, education, security, health, and everyday life. In this context, questions are being raised that how this technology can change political values, up to which extent AI can be applied and what are the reasons behind world implementing AI in politics with narrow hands? Upon launching of intelligent robots as public office holder, the ownership would be more important for security and sustainability. This stance needs to be outlook for an organized investigation of key proprietress components that may arise before or after artificial intelligent system mount in politics. In this paper, we established specific guiding measures for the proprietors of AI algorithms to be executed as state office holders.
Development of Solar Based Level 3 Fast Charging Station forElectric Vehicle
The attention given to the adoption of electric vehicle leads to the demand of fast charging technology adoption for electric car batteries. The suggested structure for fast charging station can be adopted to charge numerous cars at a time. It is a simplest model of fast charging station based on renewable energy. design strategy and parameters to implement in MATLAB/SIMULINK. Simulation is performed in MATLAB Simulink to observe the charging characteristic of a battery to obtain a desired response.
Electrical Vehicles Charging System in Parking Yards
This research paper discusses the design and implementation of EV (Electric Vehicle) Charging stations in parking lots. We consider several key factors when implementing this design especially for a country like Pakistan. We also discuss EV’s importance to environment in the coming age of technology and how it will benefit our society as a whole. International standards that are going to be used will also be mentioned when implementing the design primarily we will use the SAE (Society of Automotive Engineers) standard for designing our EV Charging System. We have explained in great detail regarding the methodology and working of our system from the Design circuit to the J1772 Plug that will be connected to the EV’s Charging module slot. The storage capacity provided due to the batteries enables us to store or supply energy to the electrical vehicle. It is to be noted that this research paper is derived from the works of others whom have been mentioned and credited and thus does not represent the effort of a single individual.
RF Communication based Wireless IndustrialControl and Monitoring System
One of the most often used techniques in major companies is panel control instrumentation. Wires connect the instruments on the control panel, such as switches and monitors, to the CPU. With these cables, the processor sends measured variables to the control panel, and the control panel sends processed variables to the processor. During wet seasons, subterranean wiring can become clogged and cause data loss if not properly maintained. This could result in a faulty procedure. To get around this, radio frequency waves are employed to send the signal from the control panel to the processing plant. Personal computers are linked to level converters and RF transmitters in industrial control rooms. This research presents the design and implementation of a wireless RF-based industrial equipment control system. An electrical system can be powered by a button press, key turn or drawing the tiled rope on the body, depending on the industry.
Multi-Defect Classification in Oranges Using YOLOv8, YOLO11, and YOLOv8m: A Comparative Study
Orange farming is among the most economically significant agricultural activities in the world, with the crop feeding global markets and supporting millions of smallholder livelihoods. Yet despite that importance, quality grading at the post-harvest stage still depends heavily on manual inspection, a slow and inconsistent process that struggles to keep pace with the demands of modern supply chains. Surface diseases such as Blackspot, Canker, and Greening are among the leading causes of post-harvest losses, and catching them early is critical to protecting fruit quality and commercial value. This paper presents an automated classification framework for orange fruit defects that evaluates three members of the You Only Look Once (YOLO) model family: YOLOv8n, YOLO11n, and an enhanced YOLOv8m trained with an extended preprocessing and augmentation pipeline. The system classifies orange images into four categories—Blackspot, Canker, Fresh, and Greening—enabling defect-specific grading decisions rather than simple pass/fail quality labelling. All models were trained and tested on a publicly available orange disease dataset under identical experimental conditions, providing a controlled multi-model benchmark. Evaluation metrics include Top-1 Accuracy, Precision, Recall, and F1-Score per class. The enhanced YOLOv8m achieved the best overall performance with 97.98% accuracy and a macro F1-Score of 0.98, followed by YOLO11n at 96.97% and YOLOv8n at 93.94%. These results show that targeted augmentation and regularization strategies can produce meaningful gains in classification performance even on small, class-imbalanced agricultural datasets.
Copyright (c) 2026 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
Analysis of Evolving Flood Patterns and Associated Damages Across Pakistan
This paper presents a comprehensive analytical review of the evolving spatio-temporal patterns of floods and associated damages across Pakistan. Understanding these evolving patterns is essential for improving flood risk management and minimizing avoidable flood-induced damage. Numerous studies have investigated different aspects of flood occurrence, flood management practices, and their socio-economic impacts. However, most have focused on individual flood events or specific flood types. Consequently, a holistic assessment of the spatio-temporal evolution of flood patterns and the associated damages under the influences of climate change and anthropogenic activities is still required. To address this research gap, an extensive literature review was conducted and historical flood data was compiled to investigate the long-term evolution of different flood types across Pakistan. Heat maps of annual peak discharges recorded at major dams, barrages, and hydraulic control points were developed to analyze the temporal and spatial evolution of flood patterns. The analysis revealed that 13 distinct flood types have been reported in Pakistan over the past 75 years, encompassing classifications based on physical origin, geographical setting, flood onset characteristics, and special triggering mechanisms. Among these, riverine, cryospheric, and rural floods have been the most persistent flood types, whereas urban flooding, Glacial Lake Outburst Floods (GLOFs), and pluvial flooding have become frequent in recent decades. The historical analysis further indicates that severe flood events with exceptionally high peak discharges were more common during the period 1970–2000, whereas recent decades have experienced a higher frequency of flood events with comparatively lower peak magnitudes. The simultaneous occurrence of multiple flood types has resulted in compound flooding in 2010, 2020, and 2022 causing exceptionally severe impacts across Pakistan. In particular, the 2022 flood affected more than 33 million people, resulted in the loss of approximately 1.16 million livestock, and damaged approximately 2.29 million houses, that makes it one of the most devastating flood disasters in Pakistan's recorded history.
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.
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.
Construction Claims Evaluation of Public Sector Projects through the Application of Building Information Modelling
Time and cost overruns in construction projects by the public sector in Sindh are characteristic of design modifications and a lack of contract management, which caused repeated claims and disputes. This paper looks at how Building Information Modelling (BIM) could be used to mitigate claims. By enhancing work and documentation. The research methodology was quantitative. A standardised questionnaire survey was used, involving 200 professionals working on public sector projects, yielding 106 valid responses. The results were evaluated using the Relative Importance Index (RII) and the Mean Score methods to identify critical claim variables and assess the claims. The findings show that BIM enhances coordination (RII = 0.67, M = 3.40), Improves the correctness of documentation (RII=0.65, M= 3.24), and minimizes errors (RII = 0.66, M = 3.28), Nevertheless, the lack of proper awareness (RII = 0.72, M = 3.59) and the absence of qualified specialists (RII = 0.71, M = 3.55) are still significant obstacles to its application in the field. The findings confirm that the inclusion of BIM in claim management helps improve transparency, collaboration, and decision effectiveness, thereby reducing the occurrence and severity of construction claims. The report recommends introducing capacity-building programs, establishing policy frameworks, and implementing pilot BIM projects to encourage digital transformation and radical sustainability in Pakistani public-sector projects.
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.