International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 623
Scientific article
Android's widespread adoption and open ecosystem make it a primary target for malware, a challenge exacerbated by internet fragmentation resulting in non-stationary data distributions across regions. This work presents AuthProtect, a scalable malware detection framework based on incremental learning and a novel permission-to-exploitation mapping approach that links 135 permissions to 25 malware development techniques. The system is validated on a balanced dataset of 82,704 benign and 82,704 malicious applications, partitioned into three geographic regions to assess robustness to distribution shifts. A similarity-based selective training strategy improves computational efficiency by training only on novel samples (cosine similarity < threshold τ), while a test-then-train mechanism enhances robustness by sequentially processing samples to avoid data exposure bias. Evaluation on four benchmark datasets (Naticusdroid, Malgenome, CICMalDroid 2020, Android Malware Dataset) demonstrates accuracy ranging from 0.9573 to 0.9992, with a maximum accuracy of 0.9982 on real-world data. We provide a comparative analysis against state-of-the-art methods and ablation studies quantifying the contribution of each component. Limitations include dependency on the completeness of permission-technique mapping and computational overhead for real-time deployment on resource-constrained devices.
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Automated Roof Top Plant Growth Monitoring System in Urban Areas
Scientific article
Innovation is progressing at a quick pace so it is required to utilize it in an each fields to have the coveted and appropriate yield. Present day's robots are winding up plainly more well-known and are progressively depend upon embrace schedule, regularly dull errands which are costly to do utilizing generously compensated labor. Because of the expanded populace and urbanization less space found for cultivating particularly in urban regions. There is a need of self-creating of slightest individual prerequisites. To fulfill ordinary demand in green vegetables and love towards their indoor and outside plants can be expert through urban rooftop gardening. This paper clarifies essential assignments of checking and detailing the development status of plants in urban rooftop gardening. We execute the customary checking of plants development. Height of the plant is one of the parameter to decide the growth of the plant. The tallness of the plant is more than the predetermined typical height in the program; robot shows that as overgrown, if there is no development discovered robot will show that as vacant slot. For identifying the development of the plant we utilize Sharp GP2D12 sensor, three white line sensors of ATMEGA 2560 FIREBIRD V to take after the arena of cultivation slots.
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Automated Wall Painting Robot for Mixing Colors based on Mobile Application
Scientific article
The final stage, which is the building paint or the adopted design, is where most real estate developers and constructors struggle. Where extensive painting is required, which takes a lot of time, effort, and accuracy from the firm doing the work. Additionally, it might be challenging to decide on the precise color grades for the design and calculate the right amount of paint to use for the job. Where these activities are extremely expensive, and the complex implementation is accompanied by worries and skepticism. These are the motivations behind the development of painting machines that blend colors. Artificial intelligence is used in the machine's design to make it efficient and quick at what it does. High accuracy is needed when selecting the proper colors, and this machine is distinguished by its ability to select the proper color tone. The color sensor (TCS34725 RGB) determines the relevance and accuracy of the desired color by comparison with the system database with the assistance of the light sensor (STM32), which measures the degree of illumination of the chosen place. By combining basic colors, this technique saves the customer the hassle of looking at specialized stores for the level of color they require. By giving the system the codes assigned to each color, it may also blend colors. The system also has the feature of controlling the machine remotely via smart phone application by enabling bluetooth and wifi features.
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Automatic IoT based plant monitoring and watering system using Raspberry Pi
Scientific article
The main objective of this proposed work is to develop an Embedded System for plant monitoring and watering system using Internet of Things, Raspberry Pi as Processor, and sensors for sensing environmental conditions. In this work, IoT concept is introduced to connect devices through Internet and facilitate information access by the users. The system can obtain accurate perception of Environmental information in agriculture field and then transmit the same to users. The system monitors different parameters like Temperature, Humidity, Soil Moisture and Intensity of light. IR sensor is fixed to check any external object entry into the field, in case of intruder detection buzzer will turn on for few seconds. The Motor fixed in the field operates both manually and automatically depending upon Moisture sensor results in soil. Motor automatically switches between on and off stage of pumping action. Results are observed either in web app and monitor.
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Automatic System Recognition of License Plates using Neural Networks
Scientific article
The urgency to increase the efficiency of recognition of car number plates on images with a complex background need the development of methods, algorithms and programs to ensure high efficiency, To solve the task the author has used the methods of the artificial Intelligence, identification and pattern recognition in images, theory of artificial neural networks, convolution neural networks, evolutionary algorithms, mathematical modeling and models characters were then statistics by using feed forward back propagated multi layered perception neural networks.. The proposed this work is to show a system that solves the practical problem of car identification for real scenes. All steps of the process, from image acquisition to optical character recognition are considered to achieve an automatic identification of plate.
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Automatic plant Irrigation Control System Using Arduino and GSM Module
Scientific article
The evolving information technology abridges the hardship in the daily life of consumers all over the world, hence the application of this knowledge in the irrigation field is necessary nowadays. The exponential growth of demand in food is due to the ever-evolving population of the world, thus it becomes necessary to expand the present area of cultivation. Considering the present situation of weather change due to global warming as a result of industrial activities, farming via irrigation is the reliable process of food production. Water remains the only source for survival for crop production, thus optimal management and proper use of water become pertinent with the ever-increasing land for irrigation. Arduino based automatic plant irrigation control system; provides a simple approach to automated irrigation. This work makes use of the GSM module for the notification of the user about the situation in the farm, this project aims to design and implement an automatic plant irrigation control system using Arduino and GSM module. In this proposed system, there are two main parts hardware and software units. Mechanical units which are the hardware unit comprises of instrumentation systems and watering irrigation systems. The equipment system is based on microcontroller, flow meter, moisture sensor, LCD, and GSM module. The software part comprises of C++ code, this is to enable the linkage between various modules. The main control of this system is the microcontroller unit that serves as the brain for coordinating control for various modules of the system, it synchronizes and operates the watering system and notifies the user about the condition of the field and watering section via GSM module. Implementation of this project will significantly help in a water-saving of about 30 – 50% as compared to the conventional watering system like the sprinkler, improve growth and discourage weeds because water will only be served to the needed area, simple method and timer-based system for automatic watering can be incorporated for efficiency.
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Automatically Extracting Name Alias of User from Email
Scientific article
Mining user identity information from emails is an important research topic in email mining. Most approaches extract an email user's name only from the header of an email, but there are often many name information in the body of emails, which are usually more suitable for representing the sender's or recipient's identity. This paper focuses on the problem of extracting email users' name aliases in the body of plain-text emails. After locating and extracting salutation and signature blocks from email bodies, we can identify the potential aliases in the salutation and signature lines, which can be directly related with the email addresses in email headers, by using named entity recognition(NER) tools. To verify and amend the potential aliases that were identified by NER tools, we propose a novel approach to extract aliases in the salutation and signature lines based on name boundary word template built on the characteristics of alias neighboring words. Results on the public subset of the Enron corpus indicate that the approaches presented in this paper can efficiently extract user's aliases from email bodies.
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Battery Management System for Solar Power Plants in Uganda: An IoT-Driven Approach
Scientific article
In Uganda, the efficiency and reliability of solar power plants are often compromised due to inadequate battery management, leading to reduced battery lifespan and suboptimal performance. To address this challenge, this project develops and prototypes a smart Battery Management System (BMS) tailored for solar power plants. The system continuously monitors key battery parameters, including voltage, load current, and temperature, while leveraging Internet of Things (IoT) technology for real-time data transmission and remote monitoring. Intelligent algorithms autonomously regulate charging and discharging cycles to prevent overcharging and deep discharge, optimizing battery performance. Testing demonstrated that the BMS significantly improved battery lifespan and energy efficiency by disconnecting charging at 100% and isolating the load at 10% discharge to prevent battery degradation. Additionally, the system disconnects power when battery temperature exceeds 30°C (ambient temperature: 25°C) and detects abnormal current levels above 0.16A to mitigate faults such as short circuits. These automated protections enhance battery reliability and longevity. By implementing proactive battery management strategies, the developed BMS contributes to more efficient and resilient energy storage systems, promoting sustainable energy development in Uganda.
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Behavioral Compatibility Analysis of Component-based Real-time System
Scientific article
For verification of component behavior compatibility in component-based real-time system, we make use of the timed automata to formally describe the component. In this way, the problem of component behavior compatibility is equivalent to whether the complementary actions can really synchronize over common channels on the system’s TA models. We then use the verification function of UPPAAL to automatically generate result, and finally conduct a case study to demonstrate how our technique works.
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Benchmarking Linear, Ensemble, and Neural Models for Entity-Level Sentiment Analysis on Twitter Data
Scientific article
Social media streams reflect opinion of public in real-time, but short and noisy tweets make it hard to attribute sentiment to entities and this paper introduces an AI/ML pipeline to classify the sentiment towards the referenced entities in twitter messages. Using the Twitter Entity Sentiment Analysis benchmark (twitter_training.csv and twitter_validation.csv), the tweets are normalized (lowercasing, punctuation, platform specific artifacts, tokenization, stop word filtering and lemmatization) and represented using TF-IDF (Term Frequency–Inverse Document Frequency) features with a maximum of 5000 terms. Machine learning models including Logistic Regression, linear SVM, Multinomial Naive Bayes, and ensemble and neural methods such as Random Forest, XGBoost, and Multilayer Perceptron (MLP) are trained on the training split and evaluated on the validation split using macro-averaged precision, recall, F1-score, and confusion matrix analysis. The results show that linear discriminative models are well suited to sparse TF-IDF spaces, with SVM and Logistic Regression providing balanced class-wise behaviour and Naive Bayes offering a computationally efficient baseline. XGBoost delivers moderate improvements over simple probabilistic models, while Random Forest achieves substantial gains through ensemble learning. The best overall performance is obtained by MLP, demonstrating that non-linear neural modeling more effectively captures complex feature interactions and entity-relevance patterns. Misclassifications are focused on the Neutral - Irrelevant boundary, resulting in instances of relevance ambiguity at the entity level in the case of sentiment and driving future extensions with context aware deep architectures and entity conditioned representations. These baselines provide support for monitoring purposes for brands and public figures as well as expose the limits of non-contextual features for sarcasm and implicit targets in Twitter discourse.
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Scientific article
Laser micromachining has become an essential tool in precision manufacturing due to its non-contact nature, high spatial resolution, and capability to produce intricate micro-features. However, identifying the optimal combination of process parameters remains challenging because of the nonlinear and interdependent effects of laser power, scanning speed, and pulse frequency on cut quality. In this study, a comparative framework is presented that benchmarks the Taguchi Design of Experiments (DoE) against a Deep Neural Network (DNN) model to predict and optimize the micromachining performance of stainless steel. A unified Cut Quality Index (CQI) was developed by combining three critical responses kerf width, heat-affected zone (HAZ), and edge chipping into a single measure of overall cut integrity. A physics-consistent dataset of 75 samples, comprising 20 literature-based and 55 synthetically generated data points, was constructed to ensure both experimental realism and statistical diversity. The Taguchi analysis using an L18 orthogonal array identified the optimal parameters as 80 W laser power, 250 mm/s scanning speed, and 60 kHz pulse frequency, corresponding to the highest signal-to-noise ratio and thermally balanced operation. The DNN model achieved strong predictive accuracy (R² ≈ 0.92–0.94), effectively capturing nonlinear parameter interactions without overfitting. The results demonstrate that while the Taguchi method efficiently identifies robust process windows with minimal experimentation, the DNN extends predictive capability across continuous, untested regions of the process space. Collectively, these findings establish a physics-informed, data-driven comparative framework for intelligent optimization of laser micromachining, with direct relevance to aerospace, biomedical, and precision micro-engineering applications.
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Beyond the grid, the Symbiosis of Virtual Power Plants and Communication Networks: A review
Scientific article
Virtual power plants (VPPs) are smart energy systems that aggregate geographically distributed energy resources (DERs), including renewable and non-renewable energy sources, energy storage devices, and controllable loads, into a single virtual plant. These plants are integrated into distribution networks to enhance grid stability and reliability. VPPs require a two-way communication framework to monitor and control all generation sources and loads, ensuring a balance between supply and demand while providing services to distribution or transmission network operators. In VPPs, various applications are used, each with specific communication quality requirements, such as reliability, latency, and bandwidth. Therefore, selecting communication technologies and protocols that meet these requirements and deliver optimal performance for each application is essential. This research paper presents a comprehensive review of the literature on VPPs, exploring their applications, communication requirements, and structural frameworks. It also examines the protocols and standards necessary to ensure reliability, security, and communication quality. Finally, the paper summarizes the global development and implementation of VPPs over the past two decades.
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Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0
Scientific article
A cyber physical system (CPS) is a complex system that integrates computation, communication, and physical processes. Digital manufacturing is a method of using computers and related technologies to control an entire production process. Industry 4.0 can make manufacturing more efficient, flexible, and sustainable through communication and intelligence; therefore, it can increase the competitiveness. Key technologies such as the Internet of Things, cloud computing, machine-to-machine (M2M) communications, 3D printing, and Big Data have great impacts on Industry 4.0. Big Data analytics is very important for cyber-physical systems (CPSs), digital manufacturing, and Industry 4.0. This paper introduces technology progresses in CPS, digital manufacturing, and Industry 4.0. Some challenges and future research topics in these areas are also presented.
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Braille Recognition using a Camera-enabled Smartphone
Scientific article
The paper proposes a method to process the image of a Braille document that interprets the raised dots on the document and converts them to their equivalent English characters. It was found that under ideal conditions of light and alignment of the Braille document with respect to the smartphone, the application can achieve more than 80% accuracy. The application can be used in the education domain, wherein users who do not understand Braille may help visually-impaired or blind students in their learning activities such as assignments and tests.
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Breast Cancer Diagnosis Improvement Based Deep Learning
Scientific article
Background: Globally, Breast cancer is the utmost predominant cancer and it affects millions of women every year. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been familiar as an efficient modality for diagnosing breast cancer. In spite of DCE-MRI modality being majorly utilized for the classification of breast cancer, the diagnostic performance is still deficient and misclassification occurs. Method: This research proposes the Deep Learning (DL) approach of Dual Attention Deep Convolutional Neural Network (DADCNN) for the classification of breast cancer into two types named benign and malignant. Initially, the DCE-MRI, Rider Breast MRI and Breast MRI datasets are utilized for estimating the effectiveness of the classifier. After collecting the dataset, pre-processing is performed by utilizing data augmentation technique. Then, the augmented data is input for the feature extraction process, which is performed by using DenseNet-121 and ResNet-101 architectures. Then, the extracted features are concatenated by using the feature fusion model and finally, classification is performed to categorize the breast cancer. The DADCNN approach deals with the long input features to selectively focus on the most relevant parts in breast cancer, so it enhances the results. The presented DADCNN approach significantly outperforms the existing methods like MUM-Net-joint prediction, UDFS + SVM, XGBoost, Multivariate Rocket and BI-RADS. The greater accuracy of the proposed DACNN approach suggests DL approach to effectively enhance the classification accuracy in breast cancer. Results: The experimental results establish that the proposed method attains greater results in all performance metrics as compared to the exiting methods like Multi-modality Network (MUM-Net) and Multivariate Rocket algorithm, The suggested DADCNN approach attains the maximum accuracy of 0.931, specificity of 0.924, sensitivity of 0.925, AUC of 0.962, PPV of 0.853 and NPV of 0.902 in breast cancer classification, which denotes that the DACNN effectively classify the cancer into benign and malignant. Concluding Remarks: The DADCNN approach deals with the long input features to selectively focus on the most relevant parts in breast cancer, so it enhances the accuracy, specificity, sensitivity, AUC, PPV and NPV in breast cancer classification.
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C2C E-commerce Risk Assessment Based on AHP and Fuzzy Comprehensive Evaluation
Scientific article
Firstly this paper gives an introduction to the current C2C e-commerce transactions in China and gives a review of relevant theories and research. Then, we proposed a new method that AHP and fuzzy comprehensive evaluation method of combining, and discuss the methods used in this risk assessment of the feasibility of e-commerce transactions. Finally, an instance of the application of this method to assess the final results prove that the application of the method of assessment of such problems feasible. With the rapid development of Internet, e-commerce as a new business model has achieved great development and progress which greatly changed the way people live and work, at the same time it gives a rise to the unlimited business opportunities. C2C is one kind of the e-commerce transactions, Internet service providers that use of computer and network technology, used to provide paid or unpaid. E-commerce platform allows two parties (mainly for individual users) in its platform to bid independently, bargaining-based online trading patterns. In recent years, China's C2C market in a rapid growth phase, this model has become the holder of a large number of small venture capital approach. However, due to the virtual nature of online transactions, buyers and sellers transaction process is often non-face environment in, on product quality, price and service and other aspects of information entirely on the buyer's estimate. Therefore, C2C e-commerce system model is a clear system of information asymmetry, the potential risk is enormous. Asymmetric information in this trading environment, to ensure smooth transactions and reduce risk, the two sides of the credit problem is particularly prominent. Meanwhile, in the current transaction before the transaction, the risk prediction and assessment has become very important. Currently, e-commerce e-commerce credit risk has become a constraint to further development of the main obstacles.
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CFD Investigation of Thermal and Pressurization Performation in LHe Tank
Scientific article
Research on thermal response of liquid helium storage tank is an important part of non-destructive storage technology for liquid helium, However there are few reports on the thermal response of liquid helium storage tanks. Taking the thermal stratification, pressure rise phenomenon and natural convection of liquid helium storage tank as research objects, based on the Lee model, the finite volume method was used for the first time to study the non-steady-state thermal response which simultaneously considering the change of liquid helium and vapor helium properties. Thus the tank pressure, temperature and flow rate distribution at different times were obtained, and the effects of evaporation rate and filling rate on the pressure rise and temperature rise of the storage tank were analyzed. The research shows that with the increase of simulation time, the liquid helium shows thermal stratification. In addition, the pressure is distributed in a stepwise manner which is lower in the upper part, and higher in the lower part, while the pressure distribution in the gas phase space is more uniform; After the liquid helium in the near-wall area is heated, it rises along the wall surface to the free liquid surface under buoyancy lift, and then flows into the main flow area of the liquid helium; As the evaporation rate increases, the temperature rise and pressure rise rate in the gas phase space of the storage tank increase, while the filling rate have less influence on the temperature rise and pressure rise rate.This work provides guidance for non-destructive storage and transportation theory of liquid helium storage tanks.
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COVID-19 Patient Health Monitoring System
Scientific article
The system proposed can be used to regular checkup of the COVID patients while maintaining the social distancing. Also, the data sensed by the sensors is directly sent to doctor, reducing the cost of paying regular visits to doctor. The Iot platform used in the system helps to transfer the real time patient’s data remotely to host device. Daily health record can be maintained and can be viewed easily on graphs charts ease for doctors to see any abrupt changes in oxygen level or rise in temperature. To track the patient health micro-controller is in turn interfaced to an LCD display and wi-fi connection to send the data to the web-server (wireless sensing node). In case of any abrupt changes in patient heart-rate or body temperature alert is sent about the patient using IoT. This system also shows patients temperature and heartbeat tracked live data with timestamps over the Internetwork.
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Calculation of Failure Probability of Series and Parallel Systems for Imprecise Probability
Scientific article
In the situation that unit failure probability is imprecise when calculation the failure probability of system, classical probability method is not applicable, and the analysis result of interval method is coarse. To calculate the reliability of series and parallel systems in above situation, D-S evidence theory was used to represent the unit failure probability. Multi-sources information was fused, and belief and plausibility function were used to calculate the reliability of series and parallel systems by evidential reasoning. By this mean, lower and upper bounds of probability distribution of system failure probability were obtained. Simulation result shows that the proposed method is preferable to deal with the imprecise probability in reliability calculation, and can get additional information when compare with interval analysis method.
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Characterization and Anti-proliferative Activity of Ethanol Extract of Tartary Buckwheat
Scientific article
Tartary buckwheat (Fagopyrum tataricum) has been consumed in China as health food and medicine. In this study, the ethanol extract (EE) of tartary buckwheat was prepared. The major flavonoid in EE was identified as rutin by HPLC-PDA-ESI/MS and the content of rutin in EE was determined as 27.4 % by HPLC. In the anticancer assay, it was found EE could inhibit the proliferation of the human hepatocellular liver carcinoma Hep G2 cell line in a dose-dependent manner. The increase of an early apoptotic population was observed by both annexin-FITC and PI staining, which suggested that EE could induce Hep G2 cells to enter into apoptosis.
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