International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 649
Enhanced Technique to Find Diabetic Retinopathy
Scientific article
Visual perception relies on the retina, which converts incoming light into interpretable neural information. Diabetic retinopathy (DR), a complication arising from prolonged hyperglycemia, is a major contributor to progressive vision impairment and often remains undetected during its initial stages. The condition manifests in retinal imagery through distinct patterns, including high-intensity and low-intensity lesion regions such as exudates and hemorrhages. This paper proposes an automated framework for simultaneous identification of multiple lesion types in retinal fundus images. The approach begins with image refinement to improve visual quality, followed by intensity-driven segmentation to ex-tract candidate abnormal regions. Descriptive statistical measures—namely mean intensity, variance, standard deviation, and entropy—are computed to characterize these regions and are subsequently utilized as inputs to an Artificial Neural Network (ANN) for classification. To enhance reliability, the method incorporates mechanisms to exclude anatomically similar structures, particularly the optic disc and vascular components, thereby reducing false detections. Evaluation results confirm that the proposed system achieves effective separation between normal and pathological cases, indicating its potential utility in supporting early-stage screening of diabetic retinopathy.
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Scientific article
This project presents an architecture for approximate matching in Content Addressable Memory (CAM) systems, which are essential for search-intensive applications such as networking and genomic analysis. Traditional CAM designs often suffer from high power consumption and reduced performance. To address these challenges, this work proposes a low complexity sensing scheme that integrates transmission gate logic and a modified inverter architecture using the SAPON technique. The primary objective is to improve power efficiency and write ability in CAM systems. By incorporating the SAPON technique, the design significantly reduces power consumption, enhancing energy efficiency while maintaining high-speed functionality. Transmission gate logic improves write ability, facilitating smoother data operations, particularly in applications requiring approximate matching. The proposed design is thoroughly validated through extensive simulation using the GPDK 45nm library in Cadence Virtuoso. The results show substantial reductions in power consumption and delay, alongside improvements in performance. The optimized CAM architecture demonstrates high tolerance for mismatches, making it ideal for applications such as DNA sequencing and network routing. This CAM design provides a scalable and energy efficient solution for modern computing environments, where performance and low power consumption are critical. Overall, this design offers a reliable and energy-efficient solution for accelerating search operations in data-driven fields, positioning it as an advancement in content-addressable memory technology.
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Scientific article
Muffler acts as noise reduction element on exhaust system. Noise from an automotive application is the major source of noise pollution. Here the transmission loss of central inlet and central outlet muffler of single expansion chamber has been compared and validated in three methods namely transfer matrix method, finite element analysis and an experimental method for this purpose an experimental setup has been built up which is based on two load method. Several researchers have worked in the area of noise attenuation on central inlet by changing the position of outlet as side outlet but no one emphasizes on offset of the central inlet and central outlet position. Thereafter the finite element analysis tool Ricardo wave 1-D and comsol multiphysics is used to evaluate transmission loss for various offset position of inlet and outlet duct of the muffler. The very purpose to improve the acoustic performance of central inlet with offset outlet pipe by measuring transmission loss of offset inlet with offset outlet with various positions by keeping same space. Finite element analysis shows that higher attenuation can be achieved by increasing offset distance of central inlet & outlet outlet towards radial direction of the expansion chamber with the variation of 0.2r, 0.4r and 0.6r. Here 'r' is radius of single expansion chamber. The result shows that high transmission loss can be achieved by increasing the offset radial distance of the inlet pipe and outlet pipe. Further higher attenuation can also be achieved in case of fixed the distance of offset inlet and outlet at 0.6r by rotating the offset outlet which is also offseted at 0.6r distance by 0o, 45o, 90o, 135o and 180o. On ward rotation from remaining 180o to 360o the behavior of wave propagation will be same what has been reflected between 0o to 180o. Transmission loss maximizes when the offset outlet is loacted at 90o. It clearly reveals that optimization can be achieved by using finite element analysis tool by using virtual protyping.
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Scientific article
The use of virtual keyboards in mobile devices such as smartphones and tablets has become an essential tool for inputting information. The sound of keystrokes has been observed in previous studies to be recorded along with ambient noises, such as those produced by uncontrolled student noise, fans, doors and windows, moving cars, and similar sources. The presence of such noises negatively affects the quality of the keystrokes signal, which in turn affects keystroke analysis. The traditional FFT-based denoising methods are vital but they are often limited by their inability to adapt to the varying characteristics of real-world audio and noises. This paper proposes an enhanced Fast Fourier Transform (FFT) with an adaptive threshold technique that reduces ambient noises. The adaptive threshold technique is developed to identify frequency bins that contain noise and set their sizes to zero or attenuate them to reduce the noise. The paper evaluates the performance of the enhanced FFT with adaptive threshold on keystrokes recorded audio and validates it through extensive experimentation. The results show that the enhanced FFT outperforms the traditional FFT in terms of speed and the amount of noise removed from the recorded audio signal, indicating a significant improvement.
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Enhancing Underwater Object Detection through CNN-based Image Enhancement and Classification
Scientific article
This research focuses on object detection using Convolutional Neural Networks (CNN) applied to underwater image datasets. Underwater images often suffer from issues such as low clarity and quality, which pose challenges for accurate object identification. To address this, the research employs image enhancement techniques, including image illumination methods, to improve image quality and facilitate object detection algorithms. Subsequently, the study developed algorithms capable of detecting objects and accurately predicting their categories. The primary objective is to achieve optimal accuracy and efficiency in underwater recognition. This research utilizes Machine Learning techniques through Tensor Flow and Image Processing to accomplish underwater object detection. Deep learning techniques, particularly feature learning, object classification, and detection, have gained significant attention and momentum. In this research we implemented different image enhancement techniques on dataset and evaluated their performance. While one metric, IQI (Image Quality Index), slightly favoured histogram equalization (HE), the other three metrics strongly favoured the enhanced version of HE known as Contrast Limited Adaptive Histogram Equalization (CLAHE).
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Ensemble-Based Modelling for Enhanced Detection of Pneumonia Disease
Scientific article
Pneumonia is a lung condition that is rather prevalent and has the potential to be lethal. The early diagnosis of this disease is absolutely necessary to cut down on the number of fatalities. The aim of this research is to provide a decision-support tool that can help professionals in the field identify cases of pneumonia. The experimental studies used two publicly available datasets in the Kaggle repository. First, experiments were conducted with the pre-trained models. Then, hard and soft voting ensemble learning approaches were implemented using the five most successful deep learning models. According to the results, the soft voting approach outperformed others, with accuracies of 98.55% and 97.26% in two- and three-class datasets, respectively. With this result, a software included this approach has been developed to assist field experts in their decision-making.
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Epipolar Geometry Estimation for Wide Baseline Stereo
Scientific article
The epipolar geometry is the intrinsic projective geometry between two views, and the fundamental matrix is the algebraic representation of epipolar geometry. Recovery of epipolar geometry is a fundamental problem in computer vision. Its importance is due to the fact that it provides relationships between corresponding point in the two images. In this paper, the problem of automatic robust estimation of the epipolar geometry for wide-baseline image pair is addressed. this problem for wide-baseline image pair is difficult because the putative correspondences include a low percentage of inlier correspondences, and it could become a severe problem when the veridical data are themselves degenerate or near-degenerate. Base on our previous work, a topological clustering(TC) is proposed to apply to fundamental matrix estimating. The TC algorithm has been demonstrated to be able to effectively eliminate the mismatches and reserve the correct matches. This advantage is extremely important to speeds up the performance of the epipolar geometry estimation and avoid the degeneracy. First, a set of match clusters are generated from the initial SIFT matches using topological clustering algorithm. Then, all the valid pairs of clusters are used to generate a series of fundamental matrix estimates and the best estimate is chosen as the solution. Six famous image pairs are used to test the proposed algorithms and the comparison with the related methods has been conducted. The compared experiments emphasize the performance of the proposed CPC algorithms.
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Scientific article
The removal of hexavalent chromium [Cr(VI)] is a challenging task due to its acute toxicity even at low concentration. In the present study, a low-cost activated carbon (LAC) was prepared from desert date seed shell by chemical activation with H3PO4 and utilized for the removal of hexavalent chromium from aqueous solution. Batch experiments were conducted to investigate the influence of operating variables such as pH, contact time, adsorbent dosage, initial concentration, co-existing ions and temperature. The amount of Cr(VI) adsorbed was found to vary with solution pH and maximum adsorption was observed at a pH value of 2.0. The extent of chromium uptake (mg g-1) was found to increase with increase in initial concentration and contact time. The applicability of the four isotherm models for the present equilibrium data follows the sequence: Freundlich > Temkin > Langmuir > Dubinin-Radushkevich. The mean free energy from the Duninin-Radushkevic isotherm model hinted that the adsorption of Cr(VI) onto the adsorbent surface follows physisorption mechanism. Thermodynamic parameters related to adsorption, Gibbs free energy change (∆G°), enthalpy change (∆H°), entropy change (∆S°), were also calculated and the negative value of ∆H° indicates the exothermic nature of the adsorption process. The considerable adsorption capacity of 99.09 mg g-1 is a signifier of the suitability of the prepared adsorbent for commercial application. The findings implicated that the adsorbent can be employed in the treatment of Cr-bearing water and wastewater.
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Estimating Missing Security Vectors in NVD Database Security Reports
Scientific article
Detection and analysis of software vulnerabilities is a very important consideration. For this reason, software security vulnerabilities that have been identified for many years are listed and tried to be classified. Today, this process, performed manually by experts, takes time and is costly. Many methods have been proposed for the reporting and classification of software security vulnerabilities. Today, for this purpose, the Common Vulnerability Scoring System is officially used. The scoring system is constantly updated to cover the different security vulnerabilities included in the system, along with the changing security perception and newly developed technologies. Different versions of the scoring system are used with vulnerability reports. In order to add new versions of the published scoring system to the old vulnerability reports, all analyzes must be done manually backwards in accordance with the new security framework. This is a situation that requires a lot of resources, time and expert skill. For this reason, there are large deficiencies in the values of vulnerability scoring systems in the database. The aim of this study is to estimate missing security metrics of vulnerability reports using natural language processing and machine learning algorithms. For this purpose, a model using term frequency inverse document frequency and K-Nearest Neighbors algorithms is proposed. In addition, the obtained data was presented to the use of researchers as a new database. The results obtained are quite promising. A publicly available database was chosen as the data set that all researchers accepted as a reference. This approach facilitates the evaluation and analysis of our model. This study was performed with the largest dataset size available from this database to the best of our knowledge and is one of the limited studies on the latest version of the official scoring system published for classification of software security vulnerabilities. Due to the mentioned issues, our study is a comprehensive and original study in the field.
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Scientific article
In this paper, we consider one-parameter exponential family and obtain the Bayes and. empirical Bayes estimators of the unknown parameter based on record values under a precaution asymmetry entropy loss function. The admissibility and inadmissibility of a class of inverse linear estimators of are studied based on upper records.
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Estimation of One-parameter Exponential Family Under Entropy Loss Function Based on Record Values
Scientific article
In this paper, we consider one-parameter exponential family and obtain the minimum variance unbiased estimator , Bayes and empirical Bayes estimators of the unknown parameter based on record values under entropy loss function. The admissibility and inadmissibility of a class of inverse linear estimators are also discussed based on upper records.
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Evaluation of Cutting-Edge Technologies for Economic Growth through Fuzzy AHP Approach
Scientific article
In the modern global economy, sustainability has emerged as a crucial foundation for achieving long-term stability and growth. Escalating environmental challenges, depletion of natural resources, and growing social expectations have made the selection of suitable technologies and innovations essential for sustainable economic progress. Yet, such decisions are often made amid uncertainty driven by technological risks, volatile markets, evolving regulations, and geopolitical instability. These factors complicate decision-making for policymakers, industry leaders, and investors, underscoring the need for resilient analytical frameworks that support informed innovative choices while mitigating risks. Achieving harmony between innovation and sustainability requires balancing economic feasibility, environmental responsibility, and social well-being. This study introduces a holistic framework for evaluating advanced technologies that contribute to economic development under uncertain and complex conditions. Utilizing the fuzzy Analytic Hierarchy Process (AHP) with Z numbers, the approach combines fuzzy logic and Z-numbers to effectively represent uncertainty and the reliability of expert evaluations. The model supports a structured multi-criteria assessment that integrates economic, environmental, and social dimensions, guiding stakeholders in selecting technologies that foster sustainable and adaptable growth. Through conceptual analysis and practical case applications, the research validates the efficiency of the fuzzy Z-AHP approach as a robust, transparent, and flexible decision-making tool for technology evaluation in dynamic economic environments. The outcomes enhance methodological advancement in sustainable development and strategic innovation management.
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Scientific article
Magnetic Resonance Imaging (MRI) is a fundamental diagnostic imaging modality that provides excellent soft-tissue contrast without ionizing radiation. However, MRI image quality is frequently degraded by noise, intensity non-uniformity (bias field), low spatial resolution, and motion artifacts, which adversely affect diagnostic accuracy and the performance of downstream artificial intelligence (AI) applications. This review presents a systematic and comparative assessment of MRI image enhancement techniques using a structured literature screening methodology inspired by the PRISMA framework. Unlike previous reviews that primarily summarize individual enhancement approaches, this study proposes a unified classification framework encompassing traditional image processing, deep learning (DL)-based, and hybrid enhancement techniques. A cross-paradigm comparison is performed using common evaluation dimensions, including enhancement accuracy, computational complexity, data requirements, generalization capability, interpretability, hardware dependency, and clinical readiness. The review further examines representative algorithms, benchmark datasets, quantitative performance metrics, clinical validation studies, regulatory pathways, and current challenges such as domain shift, explainability, and reproducibility. Emerging trends, including transformer-based architectures, self-supervised learning, multimodal enhancement, federated learning, and edge AI, are also discussed. The analysis indicates that although DL approaches consistently achieve superior quantitative performance, hybrid techniques provide a more balanced trade-off between enhancement accuracy, interpretability, computational efficiency, and deployment feasibility. This review offers a comprehensive reference by integrating methodological, technical, and clinical perspectives while identifying key research directions for developing robust, trustworthy, and clinically deployable MRI image enhancement systems.
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Experimental Study of Airlift Pump Performance with S-Shaped Riser Tube Bend
Scientific article
Airlift pump is a type of deep well pumps. Sometimes, it is used for removing water from mines or pumping slurry of sand and water or other solutions. The performance of airlift pump is affected by two sets of parameters; the geometrical and operational parameters. The geometrical parameters include pipe diameter, pump height, design of air injection system, and entrance geometry of the lifting pipe; while the operational parameters involve submergence ratio, conditions of injected air, and nature of lifted phase. Conventionally, airlift pump with bent riser tube is less efficient than that with vertically straight riser tube. However, in real life situations, the use of local riser tube bend or flexible riser tubes is considerably unavoidable. This work investigates experimentally the effects of local bends of the riser tube on the airlift pump performance. A series of experiments on a model airlift pump with three different riser tube configurations, based on the vertical position of local bends, were carried out. The local bends are in the form of an S-shaped like duct. The results showed that setting local bends of the riser tube near the air injection zone improves the airlift pump performance. However, improvement obtained in airlift pump performance is being negligible and, thus, the position of local bend of riser tube does not contribute to improvements in the performance of airlift pump.
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Exploratory Study on Hyperledger Fabric Framework: Food Supply Chain as a Case Study
Scientific article
The wide use of supply chain management systems in various business sectors encouraged researchers and those who were concerned to explore and employ efficient technologies to improve such systems. The integration of blockchain into supply chains has proved its effectiveness at increasing the customer’s trust level, as well as many other features, such as traceability, immutability, provenance awareness, etc. Moreover, the use of private permissioned blockchain networks, for instance Hyperledger Fabric (HLF), not only leverages the level of confidence, but also increases the speed of transaction execution. In this paper, an exploratory detailed study on Hyperledger Fabric framework is conducted. The study focused on the HLF network design, the consensus algorithms used in HLF, the HLF smart contracts and the transaction flow stages. Moreover, a number of illustrative case studies that used HLF into their networks designed for food supply chain management systems have been introduced. The basic design components in each of the applications are reviewed as well as the main goals and desired outcomes.
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Extraction of facial features for detection of human emotions under noisy condition
Scientific article
Affirmation of human faces out of still pictures or picture progressions is an as of now making research field. There are an extensive variety of engagements for structures adjusting to the issue of face limitation and affirmation e.g. exhibit based video coding, face conspicuous confirmation for security structures, look area, and human-PC connection. The acknowledgment and region of the face, and furthermore the extraction of facial features from the photos, are fundamental. In view of assortments in illumination, establishment, visual point and outward appearances, the issue becomes complicated. This paper presents a novel method to extract human facial features for the detection of human emotions (such as “sad”, “happy”, “sorrow” etc.) under noisy conditions. This whole work constitutes better working of a video surveillance system. For detection and extraction of facial features simple formulae are used to represent skin color models depending on the range of HSV (Hue, Saturation, Value) values used for the detection of human skin. Here HSV color model is used because it is fast as well as compatible with human color perception. Additionally, implementation of Probability Neural Network (PNN) enhances the working of the surveillance system. Utilization of PNN expands the ability of surveillance framework as it can give the yield image regardless of whether the information image contains noise in it. The proposed algorithm for the entire task is developed using MATLAB software along with suitable Image Processing Toolbox (IPT).
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Scientific article
As of late, Automatic speech recognition has advanced on account of instruments, for example, natural language processing, and deep learning, among others. It is a framework or put in another way, a gadget that changes a raw signal into computer comprehensible text. The genuine creation of speech is comprised of changes in air pressure that outcomes in pressure wave that our ear and cerebrum comprehend. The vocal tract is utilized to deliver a human speech, which is adjusted by teeth, tongue, and lips. Speech recognition alludes to a machine's ability to perceive human speech and transform it into a computer comprehensible text. Speech recognition is a magnificent illustration of good interaction between humans and computers. In this paper, we introduce the process to extricate the feature from the signal utilizing Mel-frequency cepstral coefficients. Mel-frequency cepstral coefficients are a genuinely far wide and proficient methodology for feature extraction from a sound file. This technique improved the speech recognition process and removes the distortion in the voice. In this manuscript we applied the Mel-frequency filtration process to improve speech and remove the background noise. the Therefore, the proposed methodology gives better performance in the automated speech recognition system.
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Smart healthcare IoT systems are vulnerable to cyber threats as they deal with sensitive patient information. Problems such as privacy, scalability, and delayed response to threats in distributed healthcare environments challenge centralized security approaches. To mitigate the security challenges of cloud-edge healthcare IoT systems, this paper presents FL-EZTF, a privacy-preserving, Federated Deep Learning and Enhanced Zero Trust Framework. The framework combines federated learning, Enhanced Zero Trust Architecture (E-ZTA), and Secure Access Service Edge (SASE). In this framework, lightweight deep learning models are developed locally at hospitals and various edge nodes without the need to transfer sensitive medical data. In place of raw data, model updates are sent conveniently through a trustaware federated learning process. Simultaneously, E-ZTA performs continuous authentication, micro-segmentation, and access control to rapidly contain threats. The framework is assessed using CIC-IoT-2023, IoT-23, and WESAD datasets. The experimental results show improved accuracy in detection, lower rates of false positives, a significant reduction in the latency of decisions, and enhanced containment as compared to centralized and traditional federated learning.
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Fabrication of a Porous Ceramic Material Suitable for Cost-effective Thermal Insulation of Buildings
Scientific article
The domestication of cost-effective, green, and sustainable building materials is significant towards its massive adoption in the developing countries. The feasibility of developing porous ceramics for cost-effective thermal insulation of buildings was explored in this study using waste materials including granite shifting and sawdust as well as chemical reagents including water glass and sodium hydroxide. Granite shifting and sawdust were dried, processed into powdered form, milled and sieved. Samples of porous ceramics were formulated using varying percentages by weight of granite shifting and sawdust mixed with a constant percentage by weight of water glass and sodium hydroxide in three different cases. The homogenized powder of the formulated composition was uniaxially pressed at 10Mpa. The samples were dried and then sintered in a gas kiln at 8500C for 3 hours. The result revealed water absorption (21.1−56.5%), compressive strength (1.2−7.9Mpa), bulk density (1.44−1.81g/cm3), apparent porosity (38.1−81.3%), and thermal conductivity (0.13−0.54W/m.K). These results indicated that the obtained porous ceramics is a potential material for cost-effective thermal insulation of buildings where a suitable combination of thermal conductivity, porosity, and mechanical strength is required.
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The key of fast identification algorithm of time-varying modal parameter based on subspace tracking is to find efficient and fast subspace-tracking algorithm. This paper presents a new version of NIC(Novel Information Criterion) using two-layer linear neural network learning for subspace tracking. Comparing with the original algorithm, there is no need to set a key control parameter in advance. Simulation experiments show that new algorithm has a faster convergence in the initial period.
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