International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 649
Scientific article
As a result of the harmful effects of GHG on the environment, and the increase in fossil fuel prices, the world is currently searching for sustainable alternative energy sources instead of fossil fuels. Bangladesh is a country that has an ambitious aim to rely on sustainable energy. This study discusses the viability of a 100MW PV power project in Rajshahi, Bangladesh by using RETScreen software. This includes benchmarking, emissions analysis, and financial analysis. The power generation cost of the proposed PV power plant is 0.09 $/kWh based on the benchmark assessment and the annual power provided to the national power grid is determined to be 140,155MWh. According to the emission analysis, overall perspective emissions to the surroundings will be decreased by 78,797.7 tCO2 annually. The NPV and cumulative cash flow demonstrate good results based on the financial analysis. The outcome is, the proposed project is both financially and technically feasible, as well as environmentally beneficial, and will assist the government in meeting the aim of developing clean and sustainable energy.
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Feature Selection Methods for Intelligent Software Performance Monitoring Based on Machine Learning
Scientific article
This paper presents a set of feature selection methods for intelligent software performance monitoring based on machine learning models, with a focus on improving interpretability, scalability, and adaptability in high-dimensional telemetry analysis. The research addresses limitations of traditional statistical and rule-based approaches, which are often unable to capture nonlinear dependencies and dynamic interactions in modern distributed architectures. A unified methodology is proposed that integrates several complementary techniques for adaptive feature selection in intelligent monitoring systems. These include a topology-aware method based on graph neural networks for modeling structural dependencies in microservice architectures, a correlation-driven approach for reducing feature redundancy, a multifactor fusion method combining statistical significance, temporal stability, and predictive contribution, a cost-efficient strategy for serverless environments, and a context-aware reinforcement learning approach for dynamic feature adaptation. The proposed methods are evaluated on a large-scale dataset exceeding 3.5 TB, collected from 42 real-world applications representing monolithic, microservice, cloud-native, and serverless architectures. The results show an average reduction in feature dimensionality of 37%, while maintaining over 95% predictive accuracy across multiple models. Additional improvements include, on average, a 21% increase in dependency modeling accuracy, an 18% gain in feature relevance estimation, a 26% reduction in feature instability under dynamic workloads, and up to 42% cost reduction in serverless environments, as observed across repeated experiments under controlled workload variability and consistent evaluation settings. While the results demonstrate the effectiveness of adaptive feature selection, further validation in diverse real-world conditions is required to confirm the generalizability of the proposed framework.
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Scientific article
Photovoltaic (PV) technology is crucial for sustainable energy generation, but the performance of PV systems in real-life conditions is closely associated with the material quality, degradation and the climatic conditions. While conventional poly-Si modules are already widely installed, Elkem Solar Silicon (ESS®)-based multicrystalline silicon modules are more sustainable owing to energy savings in silicon production. But little field data is available on their behaviour in the hot Indian climate. Thus, the field performance of ESS® multicrystalline silicon and conventional poly-Si modules after one year of installed operation in a rooftop grid-connected 6.71 kWp PV system at BVRIT, Telangana, India is assessed. The demonstration system comprises 28 modules (organized in four rows of seven, with 14 ESS® modules and 14 conventional poly-Si modules). Performance analysis was carried out using field-based energy yield, I–V and P–V characteristics, peak power degradation, electrical mismatch, as well as electroluminescence imaging of the modules. The findings show that both technologies had minimal degradation, with an average annual power loss of 0.3% for ESS® modules and 0.4% for conventional poly-Si modules. ESS® module technology showcased 1-1.5% higher energy yield than conventional poly-Si modules, mainly because of better high-temperature operation. At the module level, it was detected that the majority of the changes in power were due to variations in current (Isc and Imp) rather than in Voc = 37.56 V and Vmpp = 30.23 V. Electroluminescence image analysis showed only minor defects from handling, but no defects caused by material. The results confirm that ESS® modules offer higher energy yield and are more suitable for sustainable grid-connected PV systems.
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Fire and Motion Early Warning Device: Its Design and Development
Scientific article
Cases of theft and robbery of computers, CCTV equipment, and LCD projector have become more frequent in schools. In addition, fire hazards are great threat to educational institutions where expensive learning materials are kept. Such incidents could be lessened and avoided if schools are equipped with appropriate security systems capable of monitoring and informing people about the coming possible danger. Thus, the development of Fire and Motion Early Warning Device (FMEWD) is timely and relevant. FMEWD consists of a website and interconnected devices and sensors intended to provide an efficient and effective warning system for preventing incidents relating to fire, smoke, and intrusion within an office. Upon detection, the system automatically sends an email and SMS to registered users. This study used the Agile Development Model which allows features to be delivered quickly and more frequently with higher levels of predictability. Evidently, the integration of different technologies conceptualized by the researcher addresses the pressing security concerns faced by educational institutions like NEUST.
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Flexural Behavior of Reinforced Concrete Beams with High Performance Fibers
Scientific article
High-performance concrete is a specialized series of concrete designed to provide superior mechanical and physical properties that cannot be achieved through the conventional design. Using high-performance concrete in the construction field can reduce the dead weight, provide a longer span, and increase the service life. In this paper, a full experimental program is conducted to study the effect of glass and carbon fibers on the flexural behavior of reinforced concrete (RC) beams. A total of 15 RC beam specimens are prepared and divided into four main groups according to their added fiber materials, which are (S- Steel fiber), and E (E- Glass fiber), carbon fiber, and S-Glass with carbon fiber. All the reinforced fibers are used at 1.5% of the cement weight. The beams are reinforced using the fiber materials at the hinging zone then tested under concentrated static load placed at the mid-span. The results show that using high-performance fibers can improve the ultimate load capacity, ductility, and absorption energy of the RC beams.
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Flood Early Warning in Rivers Based on ESP8266 Microcontroller and Arduino Nano
Scientific article
Information about the arrival of floods in rivers must be informed as soon as possible to the community so that it can save people along the river and its surroundings from the dangers of flooding which are very detrimental. Arduino Nano and Microcontroller ESP8266 provide good performance in providing information about the arrival of floods quickly. The working system of the tool is based on a water level sensor installed in the upstream area of the river which will be received and processed by Arduino nano, then the sensor data is communicated to the ESP8266 device (as a wifi node). Furthermore, ESP8266 will send information to the Android application. This system is very cost-effective and has low power consumption. Flood information will be sent to people along the river that flows through the city and residential areas. The test results show that the current system is functioning well, and is useful for flood monitoring systems in rivers.
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Scientific article
Most digital forensic investigations involve images presented as evidence. One of the common problems of these investigations is to prove the image's originality or, as a matter of fact, its manipulation. One of the guaranteed approaches to prove image forgery is JPEG double compressions. Double compression happens if a JPEG image is manipulated and saved again. Thus, the binaries of the image will be changed based on a “previous” quantization table. This paper presents a practical approach to detecting manipulated images using double JPEG compression analysis, implemented in a newly developed software tool. The method relies on an adaptive database of quantization tables, which stores all possible tables and generates new ones based on varying quality factors of recognized tables. The detection process is conducted through image metadata extraction, allowing analysis without the need for the original non-manipulated image. The tool analyzes the suspected image using chrominance, and luminance quantization tables utilizing the jpegio Python library. The tool recognizes camera sources as well as the programs used for manipulating images with the related compression rate. The tool has demonstrated effectiveness in identifying image manipulation, providing a useful tool for digital forensic investigations. The tool identified 96% of modified images whereas the other 4% identified as false positives. The tool fixes the false positives by extracting the software information from the image metadata. With a rich sources database, forensic examiners can use the proposed tool to detect manipulated evidence images using the evidence image only.
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Four-dimensional Vector Matrix Determinant and Inverse
Scientific article
The theory of two-dimensional matrix has been popularized in multi-dimensional matrix. However applications of multi-dimensional matrix also bring space redundancy and time redundancy, we put forward a multi-dimensional vector matrix model. This is new series of study to define multidimensional vector matrix mathematics, including four-dimensional vector matrix determinant, four-dimensional vector matrix inverse and related properties. There is innovative concept of multi-dimensional vector matrix mathematics created by author with numerous applications in engineering, math, video conferencing, 3D TV, and other fields.
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Scientific article
Traditional sentiment analysis performs well in terms of polarity detection, but it cannot capture cognitive nuances needed for mental health monitoring. In this paper, we address two gaps in the current literature: the lack of task-driven guidance on how to choose transfer learning strategies and the lack of validation on cognitively rich da-tasets, other than standard polarity benchmarks. We present a systematic review of 150 transfer learning studies follow-ing PRISMA guidelines, summarising the findings into a decision framework that maps data availability, domain spec-ificity, and resource constraints to optimal model selection. We empirically validate our decision framework on a novel 4-class cognitive thought classification dataset with human-elicited samples and GPT-4 augmented data. Active learn-ing reduced annotation effort by 60% and achieved high inter-rater agreement (Fleiss’ Kappa = 0.82). According to our framework, 12 models were evaluated. BiLSTM-TF-IDF achieved 93.9% macro F1, outperforming transformer models by 0.5% with 9× less compute, supporting the medium-data/domain-specific path. Specifically, on our 4,944-sample domain-specific cognitive thought dataset using a single NVIDIA RTX 3090 GPU, BiLSTM-TF-IDF outperformed RoBERTa-base by 0.5% macro F1 (93.9% vs. 93.4%, McNemar p=0.004) via uncertainty-sampling active learning with SVM confidence threshold <0.40, while requiring approximately 9.4× less inference time and a 32× smaller model footprint (with a 6× lower peak GPU memory requirement). Uncertainty was quantified using least-confidence sam-pling (selecting samples where max class probability <0.40); the cross-entropy loss function was used for both the SVM-guided active learning and BiLSTM training. We also evaluate cross-dataset performance on IMDb and SST-2, demonstrating reasonable generalisability. Demographic analysis shows cognitive patterns that have implications for mental health. The dataset and code have been published at Zenodo (DOI: 10.5281/zenodo.17444289) to facilitate reproducible cognitive NLP research.
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Fully Automated Hydroponics System for Smart Farming
Scientific article
This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.
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Fuzzy Based Enhanced Smart Rest Room Automated Faucet System
Scientific article
Existing smart rest room automated faucet systems operate on traditional control theory, with absolute control value, irrespective of its degree of accuracy. For example, a hands-free hand wash basin may be programmed to release water when an object is about 2cm close to the infrared sensor. This absolute control value has pitfalls such as, ascertaining the quantity of water required for each operation, without activating the water flow switch to release water at maximum rate, leading to wastage of water resources. In order to develop an efficient and accurate smart rest room automated faucet system that will release water at an approximate quantity as required for varying condition, this research paper delves into employing a data-centric model for understanding and designing a smart rest room automated faucet system, that is more accurate in operation and properly utilizes water resources. To achieve this goal, we first designed a fuzzy model using MATLAB, for the proposed smart rest room automated faucet system, and then implemented the model on embedded atmel328 microcontroller, interfaced with an infrared obstacle sensor and an electronic flow control switch to automate rest room faucet activities. In order to understand and improve its operation, data logged from the system infrared sensor for 3cm distance, was mined for proper understanding of the system operational accuracy. During which the result indicated proper water utilization at various rate of water dispensation, as a function the nearness of the object to the sensor. This supports the improvement promised by the proposed system, when adopted in existing smart rest room automated faucet systems design and re-design.
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Gas Leakage Detector and Monitoring System
Scientific article
Leakage of gas is a major issue in the industrial sector, residential buildings, and gas-powered vehicles, one of the preventive methods to stop accidents associated with gas leakage is to install gas leakage detection devices. The focus of this work is to propose a device that can detect gas leakage and alert the owners to avert problems due to gas leakages. The system is based on a microcontroller that employs a gas sensor as well as a GSM module, an LCD display, and a buzzer. The system was designed for gas leakage monitoring and alerts with SMS via an Arduino microcontroller with a buzzer and an MQ2 gas sensor. The circuit contains a Microcontroller MQ2 gas sensor, buzzer, LCD display, and GSM module, when the sensor detects gas leakage it transmit the information to the Microcontroller while the microcontroller makes a decision and then forwarded a warning message to the user as SMS to a mobile phone for decision to be taken accordingly. The output of this research will be significant in averting problems associated with gas leakages now and in future.
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Gender Classification Optimization with Thermal Images Using Advanced Neural Networks
Scientific article
In this study, we investigate the effectiveness of deep learning models with thermal images for gender categorization. In order to explore the possibilities of thermal imaging as a tool for gender identification, the study focuses on two sophisticated convolutional neural network (CNN) architectures: InceptionV3 and AlexNet. Thermal imaging is a powerful substitute for traditional visual data because it provides distinct physiological insights.A collection of thermal imaging datasets was assembled, methodically preprocessed, and divided into training and testing sets. For this comparison analysis, two well-known CNNs AlexNet, a fundamental model recognised for its straightforward yet efficient design, and InceptionV3, a complex model acclaimed for its inception modules were chosen. The training subset was used to carefully refine both models so they could accurately capture the subtleties of thermal-based gender traits.Accuracy was the main criterion used to assess the performance of the revised models on the testing subset. According to our results, InceptionV3 performs noticeably better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This disparity in performance demonstrates how much better InceptionV3 is at identifying and deciphering minute thermal patterns and physiological indicators that are essential for precise gender categorization. This study highlights how sophisticated CNN architectures may improve gender categorization using thermal images, both in terms of accuracy and dependability. We provide a path for future research to investigate more intricate and integrated strategies, like multi-modal fusion and sophisticated feature extraction techniques, to further enhance the resilience of thermal-based gender classification systems by proving the efficacy of InceptionV3 over a more conventional model like AlexNet.
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Generation of Images from Text Using AI
Scientific article
Reading the words can be confusing, and it may be hard to picture what is happening. There are some circumstances where words can be misunderstood. It's much simpler to recognize text if it's displayed as an image. The use of visuals is proven to increase viewership and retention. Synthesizing realistic images automatically is a challenging undertaking, and even the most advanced artificial intelligence and machine learning algorithm has trouble meeting this standard. GANs (Generative Adversarial Networks) are just one example of a powerful neural network architecture that has shown promising results in recent years. Existing text-to-image methods can generate examples that generally reflect the meaning of the provided descriptions, but they often lack the necessary details and colorful object elements. The primary objective of our research was to explore diverse architectural methodologies with the intention of facilitating the generation of visual representations from textual descriptions. By delving into this investigation, we aimed to discover and examine various approaches that could effectively support the creation of visuals that accurately depict the content and context provided within written narratives. Our aim was to unlock new possibilities in the realm of visual storytelling by establishing a strong connection between language and imagery through innovative architectural techniques.
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Government Expenditures, Transfer Payments and Economic Growth
Scientific article
Incorporating a two-level government structure into an endogenous growth model, we distinguished between productive and non-productive government expenditures. With transfer payments considered, we showed that (1) there was an “Inverted U-shaped” relationship between the tax rate and the long-run economic growth, so was the relationship between the degree of fiscal decentralization and the long-run economic growth; (2) optimal ratios between productive and non-productive expenditures of two levels of governments, between transfer payments and other parts of expenditures of the state-level governments are needed to maximize the long-run economic growth.
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Scientific article
Automated diagnosis of Grape Black Measles (GBM) disease has become a pivotal aspect of modern agribusiness due to its efficiency and rapidity. Manual segmentation and diagnosis of GBM are intricate tasks due to time and cost constraints. In this study, we introduce Grape Leaf Doctor, a novel method for the automatic detection and severity analysis of GBM, utilizing the response surface approach and interval type 2 fuzzy logic inference (IT2FL). Firstly, we employ the DeepLabV3+ semantic segmentation model based on ResNet50 to perform pixel-level predictions on images of grape leaves affected by fungal lesions. This model enables the identification of “regions of interest” (ROIs) and the calculation of the percentage of infections (POI). Subsequently, the IT2FL rule-based system is constructed to assess the severity of disease damage based on these features. In the IT2FL system, Gaussian and trapezoidal “membership functions” (MFs) are explored for inputs and outputs to facilitate fuzzy inference and defuzzification. The severity of GBM infection is categorized into four levels: ‘Healthy’, ‘Mild’, ‘Medium’, and ‘Severe’. The experimental results on the IT2FL hold-out test dataset show a general classification accuracy of 98.34%, whereas RSM achieves 90.69%. By merging image processing and statistical modeling, the DeepLabV3+ framework of the IT2FL system can efficiently recognize GBM across varying disease risks.
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Scientific article
The basic difference between a sustainable aesthetically positive urban environment and an aesthetically negative one is in the way its component installations are rendered. The aesthetic positivity (AP) or aesthetic negativity (AN) of the whole is dependent on the aesthetics of the little parts that constitute it. Although may be functional, many electrical installations in Nigeria still lack considerable aesthetics mostly due to lack or laxity in the knowledge or practical application of basic design theories and principles. This study therefore examined how the application of design principles and theories used in graphic design can apply in electrical and design installations as a way of fostering a more aesthetic, yet functional and sustainable environments in developing West African countries using aesthetics as a key driver. Adopting a descriptive approach supported with direct observation, with a sample size of 320, respondents were purposively sampled in selected cities in Nigeria. The study showed a significant relationship between the application of graphic design theories and improved environmental aesthetics through the rendering of attractive-functional electrical/design installations. It also revealed that improved aesthetics of electrical/design installations limits negative interference which improves sustainability/safety in the built environment, hence serving as an abatement tool or technology for the alleviation of AN. This study therefore established the significance of the application of design theories and principles in achieving a more aesthetic, functional and sustainable environment, from the professionals’/ practitioners’ perspective.
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Green Functions for Sub-Laplacian on Half Spaces of the Heisenberg Group
Scientific article
Green functions for sub-Laplacian on the domains in the Heisenberg group are derived, which can be used to solve partial differential equations subject to specific initial conditions or boundary conditions. Then the integral formulas for sub-Laplace equation on characteristic and non-characteristic half spaces are given, respectively.
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Guiding Aid for Visually Impaired
Scientific article
Visual impairment is where the person either can’t see or his vision has weakened to large extent. There is no alternative technique for visually impairment, but to some extent it can be trim down with devices, smart sticks and sensors. Although many techniques are there for helping out through electronic travelling aid, cost effective and minimum hardware solution was the expectation by impaired. The device which can identify and classify the object ahead of impaired person is needed so that person can be prevented from the accident. In this paper, a unified model of YOLO (You Only Look Once) is used for detection of object ahead of camera. The proposed model is based on phenomena of detecting small object and good detection speed of yolov3 makes system more robust. Once detected, labeled objects name is converted from text to speech, so that blind person can be alerted from colliding with obstacles. This paper is one step in the direction to help them by exactly classifying, detecting and localizing target object along with providing voice based guideline. The proposed model has proved accuracy in many real time scenes.
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HKCHB: Meta-heuristic Algorithm for Task Scheduling and Load Balancing in Cloud-fog Computing
Scientific article
Cloud-fog computing has emerged as the contemporary approach for processing and analyzing Internet of Things applications due to its ability to offer remote resources. Cloud fog computing technology provides shared resources, information, and software packages, supporting distributed parallel systems in an open environment. It constructs and manages virtual machines to enhance efficiency and attractiveness. We have consistently strived to tackle challenges affecting the efficiency of cloud fog computing, including ineffective resource utilization and response times. The improvement of these challenges can be achieved through effective task scheduling and load balancing between Virtual Machines, this problem considered as NP-hard problem. This paper proposes a Hybrid K-means Clustering Honey Bee algorithm (HKCHB) to cluster Virtual Machines into two or more clusters. Subsequently, the hybrid Honey Bee algorithm is employed for task scheduling, enhancing load balance performance. The proposed algorithm is compared with other task scheduling and load balancing algorithms, including Round Robin, Ant Colony, Honey Bee, and Particle Swarm Optimization Algorithm, utilizing the CloudSim Simulator. The results demonstrate the superiority of the proposed algorithm, yielding the lowest response time. Specifically, the response time is reduced by 22.1%, and processing time is reduced by 47.9%, while throughput is increased by 95.4%. These improvements are observed under the assumption of multiple tasks in a heterogeneous environment, utilizing one or two Data Centers with Virtual Machines. This contribution gives the impression that network systems based on the Internet of Things and cloud fog computing will be improved in the future to operate within the framework of real-time systems with high efficiency.
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