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
Explainable Artificial Intelligence in Medical Image based Diagnosis: A Comprehensive Study
Artificial intelligence (AI) has made remarkable contributions to medical imaging, enhancing the accuracy of the diagnosis and its efficiency; the challenge to the adoption in clinical settings has been the lack of clarity of the deep learning models. Explainable Artificial Intelligence (XAI) is a solution to this dilemma, as it provides easy-to-understand and understandable information about the way the model arrived at its decisions. The review summarizes the latest advances in XAI in the radiology, ophthalmology, pathology, neurology, and oncology domains as well as the conventional machine learning attribution algorithms; saliency-based deep learning algorithms, including Grad-CAM, SHAP, and LIME; and multimodal models. Its main clinical uses are cancer detection, classification of Alzheimer's disease, COVID-19 diagnosis, and evaluation of retinal disease. There are both quantitative (fidelity, stability, and localization) and qualitative (interpretability and radiologist trust) evaluation strategies. Although there is significant improvement, there exist dataset quality issues, interpretability-accuracy trade-offs, and generalizability issues across clinical settings. Furthermore, a comparative study with the available literature is done in a systematically organized way the evaluation parameters are clearly developed to evaluate objectively the strengths of our approach. This comparative parameter-based parameter shows the advances of our framework with respect to generalizability, explainability quality, and clinical relevance. Our study makes a more effective justification of its usefulness than the previous methods since the comparison is based on quantifiable measures.
Copyright (c) 2025 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
Enhancing the Photocatalytic Performance of Anatase TiO2 through Vanadium, Mangenese and Nitrogen Co-doping: A DFT Study
TiO2 is a well-known photocatalyst with several potential applications. But because of its wider band gap, its optical activity is restricted to the UV region of the electromagnetic spectrum. These peak limits the photocatalytic activity of the specimen. To boost the photocatalytic activity of TiO2, it is necessary to shift its absorption peak from the ultraviolet to the visible range by reducing its bandgap. Doping of several metals and non-metals is one of the strategies adopted to decrease the bandgap of TiO2. This research article provides an overview of the effects of co-doping with Vanadium, Manganese, and Nitrogen on the structural and optoelectronic properties of anatase TiO2 using the framework of Density Functional Theory (DFT). V–N/TiO2, Mn–N/TiO2, and Mn–V/TiO2 were prepared, in which Mn and V were completely replaced at the i site, while N was at the O site. It was predicted that (V–N) doping in TiO2 decrease the bandgap from 2.13eV to 1.66eV. This decrease occurred due to the newly generated electronic states, i.e. V–3d and N–2p, below the conduction band minimum and the valence band maximum, respectively. (Mn– N) and (Mn–V) co-doping in TiO2 decrease the value of bandgap to 1.36eV and 1.22eV respectively. In these two models, the Fermi surface moved above the conduction band minimum, and thus, these models acted as N–type semiconductor. Co-doping also enhanced the optical performance of the specimen. It was observed that optical absorption increases as the model's bandgap energy decreases. The most enhanced optical absorption performance was predicted for Mn–V/TiO2 due to the effective electron-hole pair recombination.
Electro-Mechanical Characteristic Analysis of the Surface Mounted Permanent Motor
This paper presents a detailed electro-mechanical performance analysis of the three-phase brushless DC (BLDC) surface-mounted permanent magnet (SPM) motor. The BLDC SPM motor operates at the rated load and rated constant rated speed conditions. The electro-mechanical characteristics of the BLDC SPM motor include the air-gap magnetic flux distribution, mechanical torque, torque ripple, mechanical output power and electro-mechanical losses. The two-dimensional (2D) finite element analysis (FEA) is utilized to model and numerical analysis of the BLDC SPM motor.
Finite Element Analysis of the Surface Mounted Permanent Motor Without Stator Excitation
This paper presents an electromagnetic analysis of the brushless DC (BLDC) surface-mounted permanent magnet (SPM) motor using 2D finite element analysis (FEA). The no-load characteristics of the three-phase BLDC SPM motor are numerically computed, and the results are analysed in detail. The back EMF waveform of the three-phase BLDC SPM motor is computed when stator windings are not supplied with current, and its harmonic analysis is carried out using the fast Fourier transform (FFT). The cogging torque is computed without stator current excitation. The no-load electromagnetic results are discussed to analyse their influence on the performance of the three-phase BLDC SPM motor.
Artificial Intelligence–Driven Imaging Advances in Lung Fibrosis: A Comprehensive Review
Lung fibrosis is a chronic and progressive illness where there is pathologic tissue scarring, which affects lung architecture and respiratory organ functioning. It is caused by several factors, including idiopathic pulmonary fibrosis (IPF), radiation-related injury, tumour-related fibrosis, and post-COVID-19 complications, and all of them are associated with similar pathophysiology. This review investigates and summarises studies published from 2015 to 2025 on biological processes, clinical symptoms, and technological innovations in the diagnosis and monitoring of lung fibrosis. It emphasises the way that artificial intelligence (AI) and deep learning (DL) models, such as quantitative computed tomography (CT) and convolutional neural networks (CNNs), have enhanced the process of early detection, disease classification, and medical progression forecasting. The computational models, such as the agent-based and Monte Carlo simulations, which are used to study fibrotic dynamics, are also discussed in the review. In general, the combination of molecular knowledge, imaging, and AI-based systems can be considered a major next step in the creation of personalized diagnoses and better treatment outcomes in chronic fibrotic lung diseases.
Enhancing Facial Recognition Performance with Data Augmentation in Occluded Environments
Facial recognition systems frequently face challenges in accurately identifying individuals when critical facial features are obscured by occlusions such as masks, sunglasses, or scarves. These scenarios degrade the reliability of recognition models, especially in real-world environments. Data augmentation has emerged as a powerful strategy to improve model generalisation by artificially increasing dataset diversity and simulating occlusion-rich conditions. This study investigates the role of augmentation techniques, which include rotation, mirroring, zooming, and brightness adjustment, in enhancing recognition accuracy under partial and full occlusions. Experimental evaluation demonstrates that models trained with data augmentation achieve notable improvements over non-augmented baselines. For instance, average recognition accuracy improved from 72.4% to 86.3% under face mask occlusions, from 70.1% to 84.7% under sunglasses occlusions, and from 68.9% to 82.5% under scarf occlusions. When augmentation was combined with illumination normalisation, further gains were observed, with overall accuracy reaching 88.9% and F1-scores exceeding 87%. These results confirm that data augmentation substantially improves resilience against occlusions, while combined augmentation pipelines provide additional robustness in variable lighting and pose conditions. The findings highlight data augmentation as a foundational strategy for developing more resilient facial recognition systems. This work advances recognition performance in occlusion-heavy environments, with implications for applications in security, surveillance, and identity verification.
A Review on Integration of Ultrasonic Vibration in Machining of Difficult-to-Cut Materials
This review compiles and analyzes forty-five recent studies exploring ultrasonic-assisted turning, milling, drilling, grinding, and hybrid techniques across diverse industries, including aerospace, automotive, biomedical, and energy. Each study is evaluated based on material type, machine setup, input parameters, and key output responses such as tool wear, surface integrity, delamination, and cutting temperature. The review highlights substantial improvements in machinability metrics but also reveals critical limitations, including scalability issues, lack of adaptive control, limited economic assessments, and insufficient long-term tool wear analysis. The paper concludes with research gaps in real-time adaptive control systems in ultrasonic vibration-assisted machining and proposes future directions for integrating intelligent control systems, AI-driven optimization, and sustainable UVAM solutions compatible with Industry 4.0 environments.
Mini-review: Experimental Approaches for the Biomechanical Testing of Bone
Mechanical testing of bone biomechanics is crucial for assessing bone strength, quality, and fracture risk. This mini-review summarizes recent advances from nanoindentation to whole-bone compression, highlighting techniques such as in situ micro-CT with digital volume correlation, finite element modelling from clinical imaging, and biomechanical sensors. Despite progress, challenges in standardization, scalability, and clinical translation persist. Future directions focus on multiscale testing, machine learning integration, and interdisciplinary approaches to improve patient-specific diagnosis and treatment. Combining experimental, computational, and clinical methods is essential for advancing bone health research.
AI-Powered Home Automation: A Simple and Smart Living Solution
This paper explains a smart home system that uses artificial intelligence (AI) to make life easier, safer, and more comfortable. The system uses voice commands, face recognition, and augmented reality (AR) so people can control home devices in simple ways. It also processes data quickly and securely. This kind of smart home can save energy, improve safety, and provide a better experience for users. There are many systems developed previously, but everyone has its own merits and demerits. The proposed system is power efficient, cost-effective and computationally intensive. The deployment of the system is very simple, and the adaptability to the new features is very flexible. The adaptability of the proposed system makes it most prominent and is gaining attention from the community.
Step-by-Step Installation of Solar Energy and Its Maintenance
The study reviews how solar panels are a green energy solution and discusses the procedure for installing solar panels from beginning to end. As costs continue to increase, as environmental issues are raised and with geopolitical issues also involved, solar energy use has risen as an alternative to regular energy sources. The analysis points out that solar energy can reduce arguments over energy supplies, make people less dependent on rare fossil fuels and give zero emissions of gases that impact climate change. While installing a solar system, the solar panels, inverters, batteries and all the needed cables are talked about in detail, as are their individual uses. Also, for the most efficient use of energy, selecting devices that protect the environment and cooperate with solar systems is emphasized. This research explores how solar can help meet energy needs sustainably and without harming the environment, by closely inspecting each stage of installing a solar system.
Integration of Hand Gesture and Voice Command Control for Smart Wheelchairs
The development of smart wheelchair systems has gained significant attention due to the increasing demand for assistive technologies that enhance the independence and mobility of individuals with disabilities. Traditional wheelchair control methods often limit the user’s ability to perform tasks with ease. This paper proposes an innovative dual-mode control system that integrates both hand gesture recognition and voice command technology to enable more intuitive, responsive, and adaptive control of wheelchairs. The proposed system combines the strengths of both gesture and voice recognition to provide a robust solution that adapts to the needs of users with various disabilities. A detailed description of the system architecture, implementation, and evaluation is provided.
Copyright (c) 2024 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
A Novel Back-to-Back Antenna Design with Dual Vertical Polarization for Enhanced Broadband Performance
The concept of wireless power transmission was first proposed by Nikola Tesla. He suggested that he could transfer power through the air. The radio waves and microwaves in the air are captured by the antenna and transferred to the destination. The receiver antenna receives the input energy and powers up the system. However, the first rectifying antennas (rectennas) were developed, enabling the conversion of microwave energy into DC power. Rectennas have undergone significant modifications, pushed by the growing demand for Wireless Power transmission and energy generation in many applications, including IoT devices & bio-medical implants. This paper presents the novelty of rectenna design that leverages a planar antenna topology and a high-efficiency rectifier circuit. The design is optimized using a combination of simulation and experimental techniques, significantly improving power conversion efficiency (PCE) and power obtained. The effects of various design parameters, such as antenna geometry, rectifier topology and input power levels, were investigated and optimized for maximum performance. This enhanced rectenna system has a peak power conversion efficiency (PCE) of 70% and a power output of 100 mW, making it compatible for powering low-power electronic devices.
A Robust Framework for 2D Human Face Reconstruction from Half-Frontal Views in Low-Quality Surveillance Footage
This paper proposes a robust framework for reconstructing 2D human facial images from half-frontal views, primarily captured under low-quality surveillance conditions. A custom MATLAB-based Graphical User Interface (GUI) is developed to support the complete pipeline, including frame extraction, enhancement, and face reconstruction. Representative frames are extracted and enhanced for video inputs using one of three techniques: histogram equalization, contrast stretching, or logarithmic transformation. Reconstruction involves detecting a single eye from the half-frontal image, followed by horizontal flipping and concatenation to generate a symmetric full-frontal face. The reconstructed faces are validated using the Viola-Jones object detection algorithm to confirm the presence and alignment of facial features. Quantitative evaluation uses the Structural Similarity Index (SSIM) and Jaccard Index (JI) to measure image quality and geometric accuracy. The proposed method is tested on publicly available datasets and a custom-designed dataset reflecting real-world surveillance challenges such as low resolution and poor illumination. Experimental results demonstrate that the framework delivers accurate and visually coherent reconstructions with low computational overhead, making it suitable for real-time surveillance and facial analysis applications.
Deoxyribose Nucleic Acid Nucleotide Virus Classification with Machine Learning
Deoxyribose Nucleic Acid (DNA) viruses are a major focus in virology because they cause many different types of human illness. DNA viruses' biology, transmission, and pathogenicity can be better comprehended if they are first properly categorized. Recently, machine learning has proven to be an effective method for studying massive amounts of biological data, such as DNA viral sequences. Here, we give a high-level overview of utilizing machine learning to categorize DNA viral sequences. We address supervised, unsupervised, and deep learning strategies that have been used for DNA viral sequence classification. The data's high dimensionality, highly variable sections, and the requirement to differentiate between closely related viral strains are only a few of the difficulties we emphasize in DNA virus sequence classification. The FASTA Tool was used to retrieve the dataset from the NCBI database. The data collected included three human gene family sequences and six virus sequences. We employed six machine learning methods to classify the DNA viruses (Sars-Cov-1, Mers-Cov-2, Ebola, Dengue, Influenza, Synthase, Ion Channel, and Transcription Factor) with 98% accuracy.
YOLOv8 vs RetinaNet vs EfficientDet: A Comparative Analysis for Modern Object Detection
Object detection plays a vital role in computer vision. It facilitates machines to comprehend and interpret images and videos and make decisions based on visual statistics. The search for the finest object detection algorithm continues to be an important endeavour in the area of computer vision. For this purpose, this paper includes three leading models—YOLO (You Only Look Once), RetinaNet, and EfficientDet, which are thoroughly examined and analyzed for object detection. We compare these three algorithms using the COCO dataset, which mainly comprises three categories of data, which are discussed in this paper. These techniques were examined using evaluation metrics. It helps to assess which algorithm is better for object detection. For this study, we used many AI-based libraries available in Python.
An Experimental Study on Determining the Strength Properties of FRC with Numerous Mix Proportions
Extensive research is being conducted globally on incorporating fibres into concrete to enhance its mechanical properties. This study aims to evaluate the strength characteristics of fibre-reinforced concrete (FRC) with varying mix proportions. 18 concrete samples of M-25 grade were prepared, incorporating different percentages of steel and glass fibers. Six samples contained glass fibres (GF) at 5%, 10%, and 15% by weight of cement, and six contained steel fibres (SF) at the same percentages. Additionally, two samples were prepared with a combination of 5% glass fibres and 10% steel fibres and two samples with 10% glass fibres and 5% steel fibres. These were compared to a control sample without fibre reinforcement. The compressive strength of the FRC was assessed at 7 and 28 days. The findings indicate that higher percentages of glass fibers had a notably negative impact on compressive strength, while the inclusion of steel fibers had a comparatively lesser effect. The maximum compressive strength was observed by including 5% steel fibres at 28 days. However, increasing the fiber content beyond this percentage resulted in a reduction in compressive strength. The lowest compressive strength was recorded in samples containing 15% glass fibers at 28 days.
Examining the Effectiveness of Current Construction Methods in Pakistan
The construction industry in Pakistan is not updated in comparison with the international market and it also does not fulfill the demands of the local market. With the increase in the population and housing demand, construction methods are evolving to cater to the increase in the population requirement for housing and shelter. With time, the cost of the labor, material, and rent of construction equipment also increased, which negatively impacts the growth of the construction industry in developing countries like Pakistan. Therefore, modern construction methods are being explored. Modern methods include construction with the help of precast, panelized building systems, etc. The topic of this research is especially focused on analyzing the effectiveness of conventional construction techniques used in Pakistan concerning cost overrun, time overrun, and other issues in the construction industry. This research also analyzes some of the new construction methods, i.e., Industrial Building System, Modular Construction, Hybrid Construction, and Panelized Building System, which are used in the construction industry of Pakistan and how these methods are effective as compared to conventional ones i.e., masonry and Reinforced Frame structure with help of literature review and a questionnaire filled out by industry experts. This research study finds out that mostly the construction industry in Pakistan is using conventional methods, while panelized building system is used in different projects as part of the project while industrialized building system is almost neglected.
Numerical Simulation and Parametric Investigation ofIncremental Sheet Forming Process for MultilayerSandwich Panels
The multifunctional structure, i.e., multilayer sandwich panel (MLSP), exhibits properties like high rigidity, good specific strength, and lightweight and thermal resistance. Structural performance is attributed to these properties, making MLSP very favourable for producing highly reliable structures and increasing fuel efficiency due to its lightweight structure. Forming MLSP through conventional forming techniques such as deep drawing and punching is very challenging because it not only limits the formability but also requires special tooling and produces other undesirable effects. This work researched the feasibility of MLSP's formation through the single-point incremental sheet forming (SPIF) process. The SPIF process was modelled in the LS-DYNA to form MLSP into a benchmark shape. A tensile test was performed to evaluate the mechanical properties of the AL-1050. In the simulation, six more cases were also evaluated to see the effects of tool diameter, feed rate, and step size on the forming forces, formability, maximum percent thinning, etc. CATIA software generated the numerical code (NC) for the constant Z-level tool path and fed it to the CNC machine to perform the SPIF experiment on the MLSP. The larger tool diameter contributed to smaller forming forces, good formability, and less fracture area. The crack area and wrinkling tendency increase with the increase in feed rate first and then decrease with a further increase in feed rate. There was good agreement between the simulated and experimental formed geometrical shapes, proving that SPIF is feasible for forming multilayer sandwich structures.
Advancing Rainfall Prediction in Pakistan: A Fusion of Machine Learning and Time Series Forecasting Models
This study brings about an innovative approach as rainfall forecast predominantly Boolean calculation is reviewed overall for 6 major cities of Pakistan for the time span of last quarter century. The study is aimed at improving the accuracy and the reliability of rainfall forecasting by making use of the capabilities of Artificial Intelligence (AI). This research investigates the efficacy of various machine learning models, including Naive Bayes, Logistic Regression, Support Vector Machines (SVM), K Nearest Neighbors (KNN), Gradient Boosting, and time series forecasting model ARIMA (Autoregressive Integrated Moving Average), for rainfall prediction. The model gets trained using 20 years of pakistan historical data performance where improving precision is the objective. With a highly scientific evaluation against real-world datasets, the new approach shows remarkable improvements in the accuracy of rainfall prediction compared to other conventional methods. Machine learning model KNN and time series forecasting model ARIM provide the good result in term of higher accuracy and lower RMSE. This results in the combination of environmental data science for innovation of the meteorological forecasting of this polarized area where the judgments are not so accurate.
Optimizing the Corrosion Protection Performance Of Epoxy Coatings for Cu Substrates: An Electrochemical Perspective
Saline water triggers the inevitable progression of corrosion risk in marine equipment, driven by spontaneous electrochemical reactions. This study used dip coating technique to prepare copper (Cu) substrates coated with epoxy resin. The study aimed to determine the optimal coating curing temperature and the epoxy-hardener ratio to exhibit maximum corrosion resistance for the epoxy-coated Cu. A comprehensive analysis of the curing temperature within the designated 40 to 120°C range and molarity of 0.6M-1.8M was conducted. The corrosion response of Cu substrates was evaluated using the electrochemical measurements of linear polarization resistance (LPR), potentiodynamic curve, and electrochemical impedance spectroscopy (EIS). The study reveals that the corrosion resistance (Rc) notably increases until reaching its peak at the optimal temperature value of 90°C and epoxy-hardener ratio of 2:1. Furthermore, a correlation between corrosion rate and molarity increased results in the deterioration of the coated surface.
Experimental Modelling of Helical Piles Subjected to Axial Loading
In the construction sector, axial loads have traditionally been supported by piles. There hasn't been sufficient comparative study related to the economics and viability aspects of conventional and helical piles up to this point in natural soil condition. The current study was conducted to test the behavior of helical piles subjected to axial loading and the results were compared with the conventional pile counterpart. For this, a total of eighteen experiments were performed with varying number of helical piles either single or multi helical piles in a raft. Helical piles were penetrated into ground, vertical load of 512 kg was applied and the settlements were recorded with the gauges installed. The reduction in settlement was found to increase due to increasing the number of piles, inter distance between the piles and increasing the number of helices. Nine double helical piles were observed to reduce settlement by 80.5% when compared to nine conventional piles. Due to the smaller number of piles needed to support the applied axial load when employing helical piles as opposed to conventional piles, the cost was greatly lowered.
Copyright (c) 2023 https://grsh.org/journal1/index.php/ijeet/cr https://creativecommons.org/licenses/by-sa/4.0
Two-Stage Approach for Demand Side Management in Multi-Smart Grids
In the face of escalating electricity demand driven by technological advancements and a growing population, efficient management becomes imperative. This study explores demand-side management (DSM) within smart grids (SGs) to minimize customer bills, alleviate peak loads, and mitigate losses. The research employs diverse strategies and compares their effectiveness, focusing on optimal load consumption across smart microgrids. Key contributions include a DSM strategy for cost reduction, efficient peak load reduction, and active power loss minimization in smart microgrids. The proposed approach is validated through simulations, with results contributing to real-world SG implementation. The study highlights a 23.4% and 31.6% cost saving for residential and commercial customers, respectively, and emphasizes the significance of load management in enhancing operational efficiency and cost-effectiveness within the energy hub framework. The research provides valuable insights for developing advanced consumption management strategies, ensuring energy efficiency and sustainability in future power systems.
Enhancing Agricultural Operations: Big Data Analytics Using Distributed and Parallel Computing
This comprehensive research investigates using distributed and parallel computing for big data analytics in agriculture to improve farming operations' sustainability, efficiency, and innovation. The paper emphasizes how big data analytics, cloud computing, and parallel distributed processing can revolutionize the agricultural industry. The research objectives include investigating the benefits and limitations of big data analytics in precision farming and crop monitoring, identifying the constraints of integrating big data analytics in agriculture and investigating the role of frameworks such as Hadoop and Spark in processing and analyzing agricultural data for informed decision-making and optimized farming operations. The methodology used in the paper is a literature review, which draws on various sources to provide insights into the topic matter. The findings indicate that big data analytics can considerably improve precision farming and crop monitoring; nevertheless, there are hurdles to incorporating big data analytics in agriculture, such as data privacy and security concerns. According to the study, frameworks such as Hadoop and Spark are crucial in processing and analyzing agricultural data for informed decision-making and better farming operations. Overall, this study offers useful insights into the possibilities of big data analytics and distributed and parallel computing in revolutionizing the agriculture industry.
Environmental Monitoring and Agricultural Insights: Analysis of Cotton Crop Using PowerBI
To understand the variables affecting Pakistan's cotton output, our research paper study focuses on applying the PowerBI tool to analyze data related to the cotton crop. We gathered data from two fields in Rahimyar Khan and Shah Alam Shah, Matiari, Sindh, to analyze the soil moisture content, availability of fertilizer, and environmental factors to improve agricultural practices and increase crop yields. The dataset contains data from monitoring dates and factors like temperature, humidity, soil moisture content, and signal intensity. We forecast cotton crop output, improve planting schedules, and foresee possible issues like bug outbreaks using predictive analytics. The study offers practical suggestions for decision-making procedures about fertilizer application schedules, irrigation schedules, and the sustainability of cotton crops. Limitations include data quality and scalability challenges, and future research will focus on improving agricultural techniques for better cotton growing.
Enhancing Agriculture Prediction through AI and Parallel Distributed Computing: A Comprehensive Study on the Impact of Weather
This revolutionary study focuses on incorporating Artificial Intelligence (AI) and Parallel Distributed Computing (PDC) in agriculture prediction, with a specific interest in weather patterns. Modern technologies are instrumental in providing solutions to enhance agricultural efficiency and sustainability. Results from this research show that PDCs and AI have the potential to make crop yield predictions more accurate, detect and prevent diseases, and allocate resources efficiently. This paper emphasizes accurate weather forecasts for sustainable farming practices while considering multiple factors involved in decision-making processes according to expert opinions. These hybrid computing approaches are highly effective in generating detailed spatiotemporal information about agricultural systems, improving agricultural predictions' accuracy. Therefore, the study has far-reaching implications for industry players seeking to improve productivity using data-based techniques for sustainability purposes.