International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 623
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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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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Scientific article
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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Scientific article
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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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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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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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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