International Journal of Multidisciplinary Engineering In Current Research
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Cost-Effective Bim Integration For Pre-Engineered Buildings
Pre-engineered buildings (PEBs) revolutionized industrial construction via efficient manufacturing and speedy erection; however, their design optimization and cost-efficiency potential is underutilised at a moment in time powered by advanced Building Information Modelling (BIM) platforms with structural analysis software. The solution proposed in this empirical study is quantitative proved as a direct consequence of introducing BIM workflows (via Autodesk Revit and Tekla Structures) with Finite Element Analysis Tools, such as STAAD. Design simulations using Pro and SAP2000 along with four independent PEB case studies on a factory, warehouse, car showroom and sports hall providing clear spans from 15 m to 40m respectively at Central India. The average structural weight reduction, total project cost savings and construction schedule compression compared to conventional steel frame alternatives are respectively 25.6%, 27.2% and 41.9%(based on data collected from a database of fifty representative structural simulations, including six corresponding beneficial Project Documentation records + Cost audits covering ₹625 lakh in combined construction expenditure). Clash detection through BIM cut down design conflicts by 87.7% and Requests for Information (RFIs) by an astounding 86.0%. Building type, span length, and software platform are all shown to significantly affect cost savings according to ANOVA (p < 0.01). It yields an R² of 0.986 for the deflection-span relationships, validating design reliability via regression modelling The results clearly indicate that BIM Integrated design of PEB is a robust, scalable method for economical and structure optimized construction in Indian industrial and commercial building typologies.
Pre-engineered buildings; Building Information Modelling; structural optimization; cost efficiency; BIM clash detection; STAAD. Pro; finite element analysis
The rapid expansion of the Internet of Things (IoT) has transformed modern digital infrastructures by enabling seamless connectivity among billions of smart devices across healthcare, industrial automation, smart cities, transportation, agriculture, and consumer applications. While IoT improves operational efficiency and intelligent decision-making, its heterogeneous architecture, resource-constrained devices, and continuous connectivity significantly increase the exposure to sophisticated cyber threats. Conventional security mechanisms primarily rely on predefined signatures and static rule-based detection techniques, which are often insufficient for identifying zero-day attacks, evolving malware, distributed denial-of-service attacks, and anomalous device behavior. Artificial Intelligence (AI) has emerged as a promising technology for strengthening IoT cybersecurity through intelligent threat detection, adaptive authentication, anomaly detection, malware classification, behavioral analysis, and automated incident response. This paper presents a comprehensive study of AI-driven cybersecurity for IoT devices by examining the integration of machine learning and deep learning techniques into modern IoT security architectures. The proposed framework incorporates intelligent intrusion detection, continuous behavioral monitoring, secure device authentication, dynamic access control, and automated threat mitigation to enhance the overall security posture of IoT environments. Furthermore, the study discusses implementation challenges including computational overhead, model scalability, data privacy, adversarial attacks, and resource limitations associated with embedded IoT devices. A comparative analysis demonstrates that AI-based security approaches improve detection accuracy, reduce false alarms, enhance response time, and strengthen resilience against evolving cyber threats when compared with conventional security mechanisms. The findings indicate that integrating artificial intelligence with cybersecurity provides a scalable and adaptive defense mechanism capable of protecting next-generation IoT ecosystems while maintaining confidentiality, integrity, and availability of connected devices.
Artificial Intelligence, Internet of Things, IoT Security, Cybersecurity, Machine Learning, Deep Learning, Intrusion Detection System, Anomaly Detection, Malware Detection, Threat Intelligence
Nonlinear Seismic Assessment Of Reinforced Concrete Buildings
Reinforced concrete buildings in seismically active regions increasingly rely on performance-based design (PBD) rather than conventional force-based procedures, since the latter offers only an implicit and often unreliable estimate of actual structural behaviour under strong ground motion. This study evaluates the seismic performance of three reinforced concrete buildings of varying height, eight, twelve, and sixteen storeys, situated in Seismic Zone IV, using nonlinear static pushover analysis and the capacity spectrum method as prescribed in ASCE 41 17 and FEMA 440 guidelines [1]. Structural models were developed in ETABS with plastic hinge assignments at beam and column ends calibrated against experimental moment-curvature data. Pushover curves, performance points, inter-storey drift ratios, and Park-Ang damage indices were extracted for each building and compared against target performance objectives of Immediate Occupancy, Life Safety, and Collapse Prevention. The results indicate that taller buildings exhibited progressively higher concentrations of drift near the mid-height region and correspondingly elevated damage indices at beam-column joints, a pattern that raises questions about the adequacy of uniform ductile detailing across height. Building A satisfied the Life Safety objective comfortably, Building B approached the threshold with localized joint distress, and Building C exceeded acceptable drift limits at three storeys, suggesting a need for supplemental damping or stiffness modification. Comparison with five prior studies confirms that the present findings are broadly consistent with regional trends reported for similar structural typologies, while also revealing discrepancies attributable to differences in hinge modelling assumptions. The study concludes that performance-based evaluation provides a more defensible basis for seismic retrofit prioritization than code-based drift checks alone, particularly for mid-to-high-rise reinforced concrete construction in zones of high seismicity [2].
Performance-Based Design; Nonlinear Pushover Analysis; Reinforced Concrete; Seismic Vulnerability; Inter-storey Drift; Capacity Spectrum Method; Damage Index
Air pollution has become a critical environmental and public health concern due to rapid industrial growth, urbanization, and increasing vehicular emissions. Continuous monitoring of air quality is essential to control pollution levels and ensure a safe environment. This project presents the design and implementation of an Air Quality Monitoring Robot, which is capable of detecting, analyzing, and reporting air pollution levels in real time. The system is equipped with multiple gas sensors to measure pollutants such as carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), smoke, and particulate matter (PM2.5/PM10). These sensors are interfaced with a microcontroller such as Arduino or ESP32, which processes the data and displays it on an LCD screen. The robot is designed to be mobile, enabling it to travel across different locations and collect environmental data efficiently. Furthermore, the system integrates IoT technology to transmit collected data to a cloud platform or mobile application for remote monitoring and analysis. This allows users to access real-time air quality information from anywhere. The system also includes an alert mechanism that activates when pollutant levels exceed predefined safety limits, ensuring timely warnings. The proposed robot offers advantages such as low cost, portability, ease of use, and high efficiency. It minimizes human exposure to hazardous environments and can be widely used in industries, smart cities, traffic monitoring, and disaster management. Hence, this project provides a reliable and effective solution for continuous air quality monitoring and environmental protection.
Air Quality Monitoring Robot, Air Pollution, IoT, Gas Sensors, Arduino, ESP32, Environmental Monitoring, Real-Time Monitoring, Smart City, Pollution Detection
Experimental And Numerical Assessment Of Rc Shear Walls With Openings
Reinforced concrete (RC) shear walls are widely employed as the principal lateral load-resisting system in mid- and high-rise buildings, yet architectural requirements for doors, windows, ducts and service penetrations frequently necessitate openings that compromise wall continuity. This study presents an empirical investigation into the structural performance of RC shear walls containing five opening configurations-solid (no opening), central opening, eccentric opening, coupled (double) opening and diagonal opening-subjected to monotonic and cyclic lateral loading. Six wall specimens at 1:3 scale were cast, instrumented and tested under displacement controlled lateral loading in a laboratory reaction frame, with additional validation performed using nonlinear finite element models calibrated against the experimental data. Quantitative data were collected for peak lateral load capacity, initial and secant stiffness, drift ratio at yield and ultimate stages, energy dissipation per cycle, and crack propagation patterns. The results, summarised across five tabulated datasets, demonstrate that central openings reduce peak lateral strength by approximately 34 percent and initial stiffness by 41 percent relative to the solid wall, while diagonally placed openings produce the most severe stiffness degradation due to diagonal tension discontinuity. Coupled openings with connecting coupling beams partially restore ductility through frame action, achieving 78 percent of the solid wall's energy dissipation capacity. Statistical correlation analysis (Pearson r = -0.87) confirms a strong inverse relationship between opening area ratio and lateral stiffness. These findings, consistent with and extending prior numerical studies, provide empirically grounded design guidance for practitioners on opening placement, boundary reinforcement detailing and coupling beam proportioning, directly supporting the study's objective of establishing a quantitative, data-driven basis for predicting shear wall performance under realistic architectural constraints
reinforced concrete; shear wall; opening configuration; lateral stiffness; ductility; cyclic loading; drift ratio
The Wireless Notice Board using Bluetooth is a simple and efficient communication system that enables the wireless transmission and display of notices using Bluetooth technology. The primary objective of this project is to eliminate the need for manually updating traditional notice boards by providing a digital and paperless solution. In this system, a user sends a message from a Bluetooth-enabled smartphone to a Bluetooth module connected to a microcontroller. The microcontroller receives the message, processes it, and displays it on an LCD or LED display instantly. This allows notices to be updated quickly without requiring physical access to the display unit. The proposed system offers several advantages, including reduced paper usage, lower maintenance costs, faster information updates, and improved convenience. It provides a reliable and user-friendly method for displaying announcements in real time, making it suitable for environments where frequent communication is required. The system can be effectively implemented in educational institutions, offices, hospitals, railway stations, banks, shopping malls, and other public places. Overall, the Wireless Notice Board using Bluetooth is a low-cost, energy-efficient, and practical solution for modern digital communication, improving the speed and effectiveness of information dissemination while minimizing manual effort.
Bluetooth Communication, Wireless Notice Board, Microcontroller, LCD Display, LED Display, Wireless Data Transmission, Embedded System, Digital Communication, Smartphone Interface, Real-Time Information Display
Facio Secure is a Face Recognition-Based Login System designed to provide secure and password-free authentication using biometric technology. Traditional login systems rely on usernames and passwords, which are vulnerable to hacking, phishing, and brute-force attacks. To overcome these limitations, this project implements facial recognition as an alternative authentication method. The system is developed using Python, Flask, OpenCV, and the face recognition library. It captures the user’s face through a webcam, detects facial features, and generates a 128-dimensional face encoding. This encoding is then compared with stored face data in a database to verify the user’s identity. If a match is found within a predefined threshold, login access is granted; otherwise, access is denied or the user can register as a new user. The application integrates Artificial Intelligence, Machine Learning, and Web Development technologies to create a secure and user-friendly authentication system. The proposed system demonstrates real-time face detection and recognition with improved security compared to traditional password-based systems. This project highlights the practical implementation of biometric authentication and its potential to enhance digital security systems in modern applications.
Face Recognition, Biometric Authentication, Artificial Intelligence (AI), Machine Learning (ML), OpenCV, Flask, Face Encoding, Computer Vision, Secure Login System, Web Application
Biyyala Keerthi, Siliveru Meghana, Siripangi Dharani
Network Traffic Monitoring and Intrusion Detection System for Digital Forensics
A Network Traffic Monitoring and Intrusion Detection System (IDS) plays a vital role in identifying malicious activities, strengthening network security, and supporting digital forensic investigations. This project focuses on the design and implementation of a system that captures, analyzes, and records network traffic in real time while detecting suspicious activities such as unauthorized access, malware attacks, and data breaches. With the rapid expansion of networked environments, cybersecurity threats have become increasingly sophisticated and frequent. The proposed Network Traffic Monitoring and Intrusion Detection System for Digital Forensics continuously monitors network traffic, captures packets, analyzes communication patterns, and detects potential intrusions using both signature-based and anomaly-based detection techniques. These methods enable the system to identify known attacks as well as unusual network behaviors that may indicate emerging threats. The system employs packet sniffing, traffic analysis, anomaly detection, and signature-based mechanisms to recognize malicious activities. All captured network data and security events are securely stored to facilitate forensic investigations. The proposed approach enhances intrusion detection accuracy, strengthens network monitoring, and assists investigators in tracing cyber-attacks and identifying their origin. Additionally, the system maintains comprehensive logs of network activities, which are essential for reconstructing attack scenarios, tracing malicious actions, and providing reliable digital evidence during forensic analysis. Real-time alerts are also generated to notify network administrators of potential threats, enabling prompt response and effective mitigation. Experimental results demonstrate that the proposed system provides efficient real-time network monitoring, accurate intrusion detection, and reliable data logging for forensic purposes. Owing to its capability to improve network security and support digital investigations, the system is well suited for applications in enterprise networks, cybersecurity operations, and digital crime investigations. Furthermore, the proposed system can be enhanced by integrating advanced machine learning techniques to improve detection accuracy, minimize false positives, and adapt to evolving cyber threats.
Network Traffic Monitoring, Intrusion Detection System (IDS), Digital Forensics, Packet Sniffing, Signature-Based Detection, Anomaly Detection, Cybersecurity, Network Security, Traffic Analysis, Real-Time Monitoring
A. Sai Srija, G. Sindhutulani, Ch. Sujatha
In recent years, the frequency of natural and human-induced disasters, including earthquakes, fires, and floods, has significantly increased, creating major challenges for search, rescue, and disaster management operations. Deploying human personnel in such hazardous environments is often dangerous, time-consuming, and resource-intensive. This project presents the design and development of an IoT-enabled robotic vehicle for disaster surveillance and real-time monitoring. The proposed robotic system is capable of remote operation through wireless communication, allowing rescue personnel to monitor disaster-affected areas from a safe distance. An ultrasonic sensor is integrated for obstacle detection and collision avoidance, ensuring safe navigation in complex environments. A thermal sensor is employed to detect abnormal heat signatures that may indicate the presence of survivors or fire hazards. A GPS module provides real-time location tracking of the robotic vehicle, while an ESP32-CAM module enables live video streaming for continuous visual monitoring. The entire system is controlled by an Arduino microcontroller, which coordinates the operation of sensors, communication modules, and actuators to ensure reliable performance. Experimental evaluation demonstrates that the proposed system effectively performs surveillance, obstacle avoidance, live monitoring, and location tracking, thereby assisting rescue teams in improving situational awareness, minimizing human exposure to dangerous conditions, and enhancing the overall efficiency of disaster response operations.
Disaster Surveillance, Internet of Things (IoT), Robotic Vehicle, Arduino Uno, ESP32-CAM, Ultrasonic Sensor, Thermal Sensor, GPS Tracking, Wireless Communication, Search and Rescue
Anam Mehnoor, B. Bhanu Sri Reddy, G. Laxmiprasanna
Design And Implementation of Smart Vending Machine
An RFID configured vending machine is a smart automated system that uses Radio Frequency Identification (RFID) technology to enable cashless and secure transactions. Unlike conventional vending machines that rely on coins or paper currency, this system identifies users through an RFID card or tag containing a unique identification number. When the RFID card is placed near the RFID reader, radio frequency signals are used to wirelessly read the stored information. The retrieved data is transmitted to a microcontroller or processing unit, which verifies the user's identity and checks the available balance stored in the system database. If sufficient balance is available, the machine allows the user to select a product using push buttons or a keypad. Once the selection is confirmed, the controller activates the dispensing mechanism, typically operated by a servo motor, to deliver the selected item. After successful dispensing, the system automatically deducts the corresponding amount from the user's balance and updates the database. The entire transaction is completed quickly and efficiently without the need for manual cash handling. RFID-configured vending machines improve security, reduce transaction time, minimize maintenance issues associated with cash handling, and provide better inventory management. Owing to these advantages, they are widely used in educational institutions, offices, hospitals, and other public places where fast, secure, and reliable service is essential. This work presents the implementation of a Radio Frequency Identification (RFID)-based multiproduct vending machine designed to automatically dispense beverages such as coke, malt, and yogurt using an RFID prepaid payment system. After carefully analyzing the limitations of the existing vending system, a new design was developed to overcome its shortcomings. The proposed system was implemented using an Arduino Mega microcontroller, an RFID reader and RFID cards, a servo motor, a Liquid Crystal Display (LCD), and other supporting electronic components. A prototyping methodology was adopted, where both hardware and software modules were designed, tested individually, and then integrated to form the complete system. The embedded software was developed using the C programming language to coordinate the operation of all hardware components effectively. Customers can purchase products by swiping their RFID cards across the RFID reader, after which the embedded system authenticates the card, deducts the product cost from the stored balance, dispenses the selected product, and displays the remaining balance on the LCD. The developed system provides a secure, efficient, and user-friendly cashless vending solution suitable for modern automated retail applications.
Radio Frequency Identification (RFID), Vending Machine, Arduino Mega, Cashless Payment System, RFID Reader, Servo Motor, Embedded System, Microcontroller, Liquid Crystal Display (LCD), Product Dispensing, Automation, C Programming
Plagiarism detection has become an essential requirement in academic and professional environments to ensure originality and integrity of content. This project presents the design and development of a plagiarism analyzer using Python, which compares textual data to identify similarities between documents. The system utilizes Natural Language Processing (NLP) techniques such as tokenization, stop-word removal, and text normalization to preprocess the input data. The core functionality is based on similarity measurement algorithms like cosine similarity and sequence matching, which evaluate the degree of resemblance between two or more text files. Python libraries such as NLTK, scikit-learn, and difflib are used to efficiently process and analyze the text. The system outputs a similarity percentage that indicates the level of plagiarism present in the documents. This plagiarism analyzer is simple, efficient, and scalable, making it suitable for applications in educational institutions, content verification systems, and research work. It helps users detect copied content quickly and promotes the creation of original material. Plagiarism refers to copying someone else’s work without proper acknowledgment. With the growth of digital content, detecting plagiarism has become very important in academics, research, and content creation. A plagiarism analyzer helps in identifying copied or similar content between documents. This project uses Python to build a simple and efficient plagiarism detection system that compares text files and calculates similarity scores. The system utilizes Natural Language Processing (NLP) techniques to preprocess the input text, including tokenization, stop-word removal, stemming, and normalization. These steps help in improving the accuracy of comparison by eliminating irrelevant and redundant data. The processed text is then transformed into numerical vectors using techniques such as Term.
Plagiarism Detection, Natural Language Processing (NLP), Python, Text Similarity, Cosine Similarity, TF-IDF, Tokenization, NLTK, Scikit-learn, Sequence Matching, Text Mining, Document Analysis
RTL Implementation Of Ambaapb Protocol For Peripheral Communication
The Advanced Microcontroller Bus Architecture (AMBA) Advanced Peripheral Bus (APB), developed by Arm, is a simple, low-power, and low-complexity bus protocol designed for efficient communication between processors and low-bandwidth peripheral devices. This project presents the Register Transfer Level (RTL) implementation of the APB protocol to enable reliable data transfer between a master and multiple peripheral slave devices. The proposed design consists of APB master, slave, and decoder modules that support both read and write operations using a two-phase transfer mechanism comprising the setup and enable phases. The RTL architecture is implemented using Verilog Hardware Description Language (HDL) to achieve synchronous operation, low power consumption, and minimal hardware complexity. Functional verification is performed through simulation to validate address decoding, control signal generation, and data transfer operations. The simulation results confirm the correct functionality of the APB protocol, ensuring efficient communication between the master and peripheral devices with simplified timing requirements. The implemented system provides a scalable, reliable, and cost-effective solution for integrating low speed peripherals in embedded systems and System-on-Chip (SoC) applications.
AMBA APB, Register Transfer Level (RTL), Verilog HDL, Bus Protocol, APB Master, APB Slave, Address Decoder, Read/Write Operations, Embedded Systems, System-on-Chip (SoC), Functional Verification
Bhanavath Kavya, Domala Meghana, Chindam Akshaya
Vehicle security and car hacking detection using wired and wireless communication
Modern vehicles rely heavily on Electronic Control Units (ECUs) and in-vehicle communication networks such as the Controller Area Network (CAN) bus, making them increasingly vulnerable to cyberattacks. This project presents a secure and efficient vehicle security system that detects and prevents malicious activities on the CAN bus using an Intrusion Detection System (IDS) integrated with cloud and blockchain technologies. The proposed system continuously monitors CAN messages using an ESP32 microcontroller interfaced with a CAN module. The collected data is analyzed in real time to identify abnormal patterns or unauthorized message injections. Upon detecting a potential attack, the system immediately triggers alerts through visual or audio indicators such as LEDs or buzzers. Additionally, the alert information is transmitted wirelessly using Wi-Fi or GSM to a cloud server for remote monitoring. To ensure data integrity and security, the detected attack logs are stored in a blockchain ledger, making them tamper-proof and reliable. The incident response center receives notifications and can take further actions based on the classified reports. A prototype model using a toy car is developed to demonstrate the practical implementation of the system. This setup effectively simulates real vehicle communication and attack scenarios in a controlled environment. The combination of wired (CAN bus) and wireless (Wi-Fi/GSM) communication enhances the flexibility and scalability of the system. The proposed solution provides a low-cost, real-time, and reliable approach to vehicle cybersecurity. It not only detects intrusions but also ensures secure data storage and remote accessibility
Vehicle Cybersecurity, Controller Area Network (CAN) Bus, Intrusion Detection System (IDS), Electronic Control Unit (ECU), ESP32, Blockchain, Cloud Computing, Wi-Fi, GSM Communication, Real-Time Monitoring
Valorisation Of Agricultural Waste Ash For Sustainable Concrete
The construction industry's dependence on ordinary Portland cement (OPC) has raised serious environmental concerns owing to the large carbon dioxide emissions and energy consumption associated with clinker production. Simultaneously, agricultural activities generate enormous volumes of biomass residues such as rice husk, sugarcane bagasse, palm oil waste, corn cob, and wheat straw, which are often disposed of through open burning or landfilling, creating additional ecological burden. Converting these residues into pozzolanic ashes for partial replacement of cement offers a dual solution reducing clinker demand while valorising agro-waste. This review paper critically synthesises past experimental and analytical studies on the effect of agricultural waste ash (AWA) on the fresh and hardened properties of concrete, including workability, setting time, bulk density, compressive strength, split tensile strength, flexural strength, and durability indicators such as water absorption, chloride penetration, and sulphate resistance. The review draws upon rice husk ash (RHA), sugarcane bagasse ash (SCBA), palm oil fuel ash (POFA), corn cob ash (CCA), and wheat/straw ashes as representative agro-wastes. Findings across the literature consistently indicate that ash fineness, silica content, and optimum replacement dosage (typically 10–20%) govern performance, with excessive replacement beyond 25–30% generally reducing workability and strength due to increased water demand and unreacted residue. The paper further presents a meta-analytical comparison of reported strength trends, identifies methodological inconsistencies across studies, and highlights research gaps concerning standardisation, long-term durability, and machine-learning-based prediction models. The review concludes that agricultural waste ash is a technically viable and environmentally beneficial supplementary cementitious material (SCM), provided its physicochemical variability is properly characterised and controlled.
Agricultural waste ash; Supplementary cementitious material; Rice husk ash; Sugarcane bagasse ash; Palm oil fuel ash; Compressive strength; Sustainable concrete
Face Mask Fiber-Reinforced Recycled Aggregate Concrete With Silica Additives: A State-Of-The-Art Review
The special issue emphasizes the unprecedented amount of single-use face mask (FM) waste produced by the global pandemic, which brings a great environmental problem that relies on emerging engineering solutions. The simultaneous erosion of natural aggregates reserves has expedited considerable curiosity towards recycled aggregate (RA) concrete as a inexperienced constructing materials. The present experimental research investigates against the mechanical and durability performance of concrete using different dosages (0.5 to 2.0 kg/m³) of polypropylene face mask fibres, 25–100% recycled coarse aggregates (RCA), 15–50% recycled fine aggregates (RFA), and SF at varying levels (5–20% cement replacement). Five concrete mixes including a control were designed, cast and tested at 7, 14, 28 and 56 days curing intervals. The mechanical properties tested were compressive strength, split tensile strength and flexural strength, while the durability was examined via water absorption, chloride ion penetration, carbonation depth and acid attack. Statistical analyses including one-way ANOVA, Pearson correlation matrices, and regression modelling validate statistically significant performance differences (p < 0.001). The results show that mix M2 (50% coarse RA, 25% fine RA,10% SF and 1.0 kg/m³ FM fibres) produced a satisfactory compressive strength of 36.1 MPa at 28 days, which is only 6.2% lower than the control, and approximately 54.6 kg/m³ of CO₂ savings. The most importantly compensating additive for the physics force reaction caused strength loss in the recycled aggregate systems, apparently identified as silica fume. The study proves the technical feasibility and environmental sustainability of a triple-component waste integration in structural concrete application.
Face mask fibre concrete, recycled aggregate, silica fume, sustainable construction, compressive strength, durability, eco-friendly concrete
Prediction of Parkinson’s Disease Using Machine Learning: A KNN-Based Classification Framework
Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra, producing characteristic motor and non-motor symptoms in millions of individuals worldwide. Early detection is clinically critical, yet traditional diagnosis is retrospective, subject to inter-rater variability, and ill-suited to mass screening. This paper presents a machine learning–based classification framework for PD prediction from biomedical voice measurements sourced from the UCI Parkinson’s dataset. The proposed system employs a K-Nearest Neighbors (KNN) classifier augmented with systematic preprocessing, MinMaxScaler feature normalization, and Random Over Sampler for class imbalance correction. Performance is benchmarked against Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and XG Boost. Hyperparameter tuning over k ∈ {1, 3, 5, 7, 9, 11, 13, 15, 20} identified the optimal KNN configuration. The final model was serialized and deployed via a Flask web application, realizing an end-to-end, scalable clinical decision support pipeline for early, automated Parkinson’s disease prediction
Parkinson’s Disease; K-Nearest Neighbors, Machine Learning; Feature Scaling; Random Over sampler; Predictive Diagnosis; Biomedical Voice Measurement; Flask Deployment
Mohammed Muqtadir Ahmed, Md Abdul Junaid, Abdul Haq
Alzheimer’s Disease Detection Using Deep Learning
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Early diagnosis is essential for effective treatment and management. Magnetic Resonance Imaging (MRI) is widely used to detect structural brain changes associated with AD; however, manual analysis of MRI data is time-consuming and prone to errors. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for automated MRI-based segmentation and classification of Alzheimer’s Disease. This paper reviews recent deep learning approaches for AD diagnosis, covering commonly used datasets, preprocessing techniques, segmentation methods, classification models, and evaluation metrics. The study also discusses key challenges such as limited datasets, computational complexity, and model interpretability. The findings highlight the potential of deep learning to improve the accuracy, efficiency, and reliability of Alzheimer’s Disease diagnosis, supporting early detection and better clinical decision-making.
Alzheimer’s Disease, Deep Learning, Convolutional Neural Network, MRI Images, DenseNet169, ResNet50, Medical Image Analysis
Syed Arshad, Shah Mohammed Asim, Sayeed bin abdul hadi wahlan
Skin Cancer Prediction Using Deep Learning Techniques
Skin cancer is one of the most prevalent and rapidly increasing forms of cancer worldwide, where early and accurate detection is essential for improving survival rates. Conventional diagnostic methods, including dermoscopic examination and biopsy, are reliable but often time-consuming, expensive, and dependent on the expertise of dermatologists. Recent advancements in artificial intelligence have enabled the development of automated diagnostic systems that can assist in early detection and improve clinical decision-making. The objective of this study is to design and evaluate an automated skin cancer detection system using machine learning and deep learning techniques for the classification of dermoscopic images into multiple categories of skin lesions. The proposed approach aims to enhance diagnostic accuracy while reducing reliance on manual interpretation. The study utilizes a dataset comprising seven classes of skin lesions, including melanoma, basal cell carcinoma, and various benign conditions. Multiple models, namely Convolutional Neural Network (CNN), EfficientNetB0, Logistic Regression, Decision Tree, Random Forest, and XG Boost, were implemented and compared. Preprocessing techniques such as image resizing, normalization, and data augmentation were applied to improve model generalization. Model performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, confusion matrix, and ROC analysis. Experimental results demonstrate that ensemble learning approaches outperform deep learning models in this study. XG Boost achieved the highest classification accuracy of 97%, followed by Random Forest at 96%, while Logistic Regression also showed competitive performance. The findings highlight the effectiveness of machine learning techniques in automated skin cancer detection and their potential to support dermatological diagnosis in clinical settings.
Skin Cancer Detection, Machine Learning, Deep Learning, CNN, Random Forest, XG Boost, Medical Image Classification
Shaik Anwar, Osman Syed, Syed Imdadullah Hussaini
Automated Knee Osteoarthritis Classification from X-ray Images Using Deep Learning
Early and accurate diagnosis of knee osteoarthritis (KOA) is essential to slow disease progression and improve patient outcomes. The standard radiographic indicator, the Kellgren– Lawrence (KL) grading system, is time consuming and subjective. This paper surveys and advances automated KOA severity classification using deep learning. We leverage large public datasets (e. g. Osteoarthritis Initiative (OAI) with ~8K X-ray images) and strong convolutional networks (pretrained ResNet, EfficientNet, etc.) to build an ensemble classifier. The proposed method uses image preprocessing (resizing, ROI extraction) and on- the-fly augmentation (random flips and rotations) during training, with transfer learning and a mix- voting ensemble strategy. We employ standard cross-entropy loss with stratified 5-fold cross- validation. Evaluation metrics include accuracy, precision/recall, F1- score, ROC-AUC, and Cohen’s kappa. Our ensemble achieves ~76–77% accuracy (F1 ≈0.76) on 5-class KL grading, outperforming individual CNN models. Explainable AI (Grad- CAM) highlights that the model focuses on clinically relevant joint space regions. Comparison with recent literature shows our results are competitive with state-of- the-art (e g. Pi et al.’s ensemble and Mohammed et al.’s ResNet approaches). We discuss limitations (class imbalance, generalizability) and future work (multi-modal imaging, larger data, improved architectures).
Knee Osteoarthritis, Deep Learning, Convolutional Neural Networks, Kellgren–Lawrence Grading, Transfer Learning, Ensemble Learning, Grad-CAM, Explainable AI
Mohd Mujeeb Khan, Mohammed Adeeb Tabraze, Mohd Kamran Hussain
Enhancing Thyroid Disease Prediction: Integrating Machine Learning Models for Improved Diagnostic Accuracy and Interpretability
Thyroid disorders represent a substantial global public health burden, frequently remaining undetected until significant clinical complications arise. This paper presents a comprehensive machine learning framework for early and accurate thyroid disease prediction, systematically integrating four established classification algorithms: Logistic Regression, Random Forest, Gradient Boosting, and XG Boost. The proposed system addresses the critical challenge of class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE), and employs rigorous preprocessing including missing value imputation, categorical encoding, and recursive feature elimination. Feature interpretability is operationalized via SHAP (Shapley Additive Ex Planations) values, enabling clinical transparency and physician trust. Experimental evaluation on the UCI Machine Learning Repository thyroid dataset demonstrates that the ensemble-based XG Boost model achieves 98.6% accuracy, 97.8% precision, 98.1% recall, and a macro F1 score of 97.9%, outperforming all individual classifiers evaluated. The framework further incorporates a concept drift monitoring module to maintain adaptive model performance in dynamic clinical environments. This integrated solution provides a robust, interpretable, and clinically deployable tool for thyroid disease screening and diagnosis support.
Thyroid Disease Prediction, Machine Learning, XG Boost, Random Forest, Gradient Boosting, Logistic Regression, SMOTE, SHAP, Explainable AI, Feature Engineering, Ensemble Learning, UCI Repository, Clinical Decision Support, Concept Drift, Interpretability
Danish Arfan Khan, Ghulam Mohammed Khan Asim, Mohammed Abdul Noman
DDoS Detection Using Packet Flow Analysis
Distributed Denial of Service (DDoS) attacks are one of the major threats to modern network infrastructure, where attackers attempt to make services unavailable by generating a large volume of malicious traffic from multiple distributed sources. The rapid growth of cloud computing, Internet of Things (IoT), and high-speed networks has significantly increased the complexity and frequency of such attacks. Traditional signature-based detection techniques are ineffective against evolving and unknown attack patterns, making them unsuitable for dynamic network environments. It proposes a lightweight DDoS detection system based on packet flow analysis combined with heuristic and rule-based techniques. The system analyzes flow-level features such as packet rate, byte count, flow duration, and source IP distribution to detect abnormal traffic behaviour without inspecting packet payloads, thereby reducing computational overhead. Entropy-based analysis and statistical thresholds are used for anomaly detection, while a risk scoring mechanism classifies traffic into different severity levels. Experimental results demonstrate high detection accuracy, low false positive rate, and efficient real-time performance, making the proposed approach suitable for deployment in enterprise, cloud, and IoT environments.
DDoS detection, Packet flow analysis, Heuristic methods, Rule-based detection, Entropy analysis, Risk scoring, Network security, Real-time detection
Mohd Sohail, Salam Bin Saleh Bashar, Omer Mohiuddin
Detection Of Malnutrition In Children Through Visual Diagnosis Pediatric Images Using Deep Learning
Malnutrition is a major public health challenge affecting millions of children worldwide, particularly in developing countries. Early detection is crucial to prevent severe physical, cognitive, and developmental complications. Conventional diagnostic methods rely on anthropometric measurements such as Body Mass Index (BMI), height-for-age, weight-for-age, and Mid-Upper Arm Circumference (MUAC), which require trained healthcare professionals and medical infrastructure. To address these limitations, this study proposes a deep learning-based approach for automated malnutrition detection using pediatric facial and body images. The proposed system employs a Convolutional Neural Network (CNN) to automatically extract discriminative visual features and classify children into three nutritional categories: normal, moderately malnourished, and severely malnourished. Image preprocessing and data augmentation techniques are incorporated to enhance model robustness and improve classification performance. Experimental results demonstrate that the proposed model achieves an overall classification accuracy of 94%, highlighting its effectiveness in image-based nutritional assessment. By analyzing visual indicators such as facial appearance, thin limbs, and reduced muscle mass, the system provides a non-invasive, rapid, and cost-effective screening solution. This research demonstrates the potential of integrating deep learning with computer vision to support healthcare professionals and community health programs, enabling accessible, scalable, and real-time malnutrition screening in resource-constrained environments.
Malnutrition Detection, Deep Learning, CNN, Visual Diagnosis, Pediatric Images, Healthcare AI, Image Classification
Ms. Ayesha Siddiqua, Ms. Bushra, Ms. Farwa Banu
Ai Framework For Fetal Health Risk Prediction
Cardiotocography (CTG) is a widely used obstetric technique for monitoring fetal heart rate and uterine contractions during pregnancy. Traditional methods of analyzing CTG recordings through manual observation are often subjective, time-consuming, and prone to inconsistencies between clinicians. To address these limitations, this paper proposes an AI-based framework for automated fetal health risk prediction using advanced machine learning algorithms. The proposed system analyzes 2126 CTG recordings sourced from the University of California, Irvine and the University of Porto datasets to classify fetal conditions into three categories: Normal (1), Suspect (2), and Pathological (3). The framework employs a Random Forest Classifier as its primary model, supplemented by other algorithms including Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, and SGD Classifier. A Flask-based web application was developed to deploy the trained model, enabling healthcare professionals to receive real-time diagnostic feedback by inputting CTG parameters directly. The Random Forest model achieved a classification accuracy of 94.6%, demonstrating its robustness in identifying fetal distress patterns. The system supports Exploratory Data Analysis, Feature Correlation Visualization, and comparison of algorithm performance. This framework significantly reduces human error, enhances prenatal diagnostic consistency, and provides a scalable solution suitable for resource-limited healthcare environments. The overall architecture of the proposed AI-based fetal health prediction framework is illustrated in Fig. 1, highlighting the complete workflow from CTG data acquisition to real-time fetal health prediction.
Fetal health prediction, Cardiotocography, Artificial intelligence, Machine learning, Predictive modeling, Clinical decision support
Kandada Sai Nithin, Gulam Muhammad Muzzamil, Ansar Ahemd
Machine Learning-Based Classification of Mental Health Severity Levels Using Mood Swings Dataset
Mental health disorders have become a significant concern due to increasing stress, anxiety, and depression across diverse populations. Early detection of mental health conditions is essential for timely intervention and improved well being. This paper presents a machine learning-based framework for classifying mental health severity levels using structured behavioral data from the Mood Swings Dataset. The proposed system utilizes Decision Tree, Random Forest, and Extreme Gradient Boosting (XG Boost) algorithms to classify mental health conditions into Low, Medium, and High categories. Data preprocessing techniques including missing value handling and categorical encoding are applied to improve model performance. Experimental results demonstrate that XG Boost achieves the highest accuracy compared to other models. The system is deployed using a Flask-based web application to provide real-time mental health prediction. The proposed framework offers a scalable and efficient solution for automated mental health assessment.
Mental health prediction, machine learning, XG Boost, classification, healthcare analytics
Syed Ajmal Hussain, Syed Abdul Muddassir, Meer Mohammed Ali
Bitcoin Price Forecasting Using Time Series Analysis
The pre-eminent cryptocurrency globally, Bitcoin, facilitates secure online transactions, ensuring financial privacy during swift cash transactions. Recent years have witnessed heightened consumer interest in the Bitcoin ecosystem. The research objective is to scrutinize two prominent approaches for cryptocurrency price forecasting: one leveraging time series methodology using Prophet and the other incorporating sentiment analysis from social media, particularly Twitter. A novel CNN-LSTM layer is proposed for comprehensive evaluation. The algorithms undergo training on real-time financial data streams, and performance is assessed through metrics like accuracy score, Mean Absolute Percentage Error, and R2 Score. Findings reveal that the integration of sentiment analysis with the CNN-LSTM pipeline yields significantly enhanced accuracy of 0.94 on the training data and 0.91 on the testing data in aligning with price trends compared to the Prophet algorithm over Bitcoin closing prices.
Bitcoin forecasting, Time-Series Analysis, LSTM, Deep Learning, Cryptocurrency Prediction, Min Max Normalization, ROC Curve
Syed Mutallib Quadri, Mohd Shahab Uddin, Syed Anas Ali
Alkali-Activated Construction And Demolition Waste For Sustainable Geotechnical Applications: A Systematic Review And Meta-Analysis
The unprecedented generation of Construction and Demolition (C&D) waste across the globe has been caused by the accelerating urbanization and the need for infrastructure renewal, challenging sustainable waste management and natural-resource depletion. In recent geotechnical studies, low carbon alkali activation has been highlighted as a promising pathway to converting inert C&D fractions, in particular recycled concrete aggregate (RCA), crushed clay brick, ceramic, and mortar fines into structural stable fills, road bases, and subgrade reinforcements. Abstract This review synthesizes and critically evaluates research conducted over the last 10 years (2015–2025) on the geotechnical performance of alkali treated C&D waste using a meta-analytical approach integrating results from more than 30 unique studies. The reviewed literature revealed that alkali activated C&D systems most often treated with sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃), and sometimes co-blended with fly ash or ground granulated blast-furnace slag (GGBS) achieve UCS values from 1.0 to 37 MPa, CBR improvements of 200–550%, and resilient modulus values meeting or exceeding those of conventional cement-stabilized pavement specifications. The microstructural mechanism is uniformly ascribed to the formation of calcium-aluminosilicate-hydrate (C–A–S–H) and sodium-aluminosilicate-hydrate (N–A–S–H) gels, which glue the heterogeneous waste matrix into a dense, low-permeability frame. Conversely, the literature reveals major inconsistencies: lack of long-term durability data, variability of C&D source composition, lack of field-scale validation, and lack of standardized design protocols. Findings From the structured survey, methodology and critical analysis, we provide recommendations for future research for industry-scale implementation of alkali-treated C&D waste for sustainable geotechnical structural applications.
Construction and demolition waste; alkali activation; geopolymer stabilization; recycled concrete aggregate; unconfined compressive strength; subgrade improvement; sustainable geotechnics.
Copyright (c) 2026 International Journal of Multidisciplinary Engineering In Current Research
Smart Self-Healing Materials For Sustainable Construction: Progress And Challenges
Self-healing smart materials are a revolutionary class of engineered materials that can autonomously detect and repair physical damage to various structures with little or no external intervention, similar to the regenerative properties of biological systems. This review paper presents a meta-analysis of more than 200 relevant primary studies focused on the use of self-healing materials in the sustainable development, with literature published from 2005 to October 2024. This review focuses on three main types of self-healing systems: intrinsic (i.e. reversible chemical bonding), extrinsic microencapsulated healing agent (single-shot) and vascular network-type where the mechanism allows multiple cycling of repair. The major results show that microbial or polymeric-based self healing concrete can heal 80–95% of compressive strength following crack formation, and self-healing polymers restore nearly all tensile properties at ambient thermal conditions. Recent research advances in the areas of both shape-memory alloys and ionomer-based composites also further extend infrastructure usage to bridges, tunnels, pavements and coastal structures. The report demonstrates that maintenance interventions can be reduced enough to provide lifecycle cost savings of 30-50%, while embodied carbon savings of 15-25% are achievable over structural lifespans of 50 years. However, challenges pertaining to scale-up, long-term field usage and standardisation have yet to be solved. This paper reviews the published work, highlights key gaps in knowledge, and suggests opportunities for utilising self-healing smart materials in relation to wider sustainable infrastructure policy and praxis.
self-healing materials, smart infrastructure, sustainable construction, microcapsule healing agents, intrinsic self-repair, vascular healing networks, lifecycle cost reduction
Comparative Investigation Of Pre-Engineered Building Erection Techniques For Improved Safety And Time Efficiency
Pre-Engineered Buildings (PEB) have become an innovative construction best practice worldwide due to PEBs' speed of delivery, structural in efficiency, and cost-effective advantages over traditional steel construction. The phase of erection is the most important part when it comes to execution of PEB projects, in this phase the technique of construction plays an important role since it affects time, safety of workers and the cost of the overall project. This empirical research compared four common techniques to erecting PEBs: Conventional Crane-Based Erection (CCBE), Lift-Slab/Lift-Up Method (LSM), Modular Pre-Assembly Erection (MPAE), and Hybrid Mechanized Erection (HME). We collected data across 12 PEB project sites in India during 2023–2025, which include industrial warehouses, manufacturing plants, and logistics hubs with a project area varying from (2,500 m² and 18,000 m², respectively). We measured quantitatively erection duration per sqm, manpower utilization, safety incident frequency rate (SIFR), equipment downtime, cost/ton structure steel erected. ANOVA, Pearson correlation, and regression modeling were used for statistical analysis to assess differences in performance between techniques. The results demonstrated that Modular Pre-Assembly Erection was fastest; 34.7% less erection time than Conventional Crane-Based Erection, while Hybrid Mechanized Erection had Lowest SIFR of 0.42 incidents per 100,000 man-hours The low Pearson correlation r = −0.81, with P = 0.001, regarding mechanization level and number of safety incidents indicates the solution to automating methods of operation should correctly contribute to substantial factory safety enhancements. By helping in the selection of appropriate erection strategies that meet site constraints and scales of a project, these findings provide actionable real-world utility to construction managers, structural engineers, and policymakers.
Pre-Engineered Buildings, Erection Techniques, Construction Safety, Time Efficiency, Modular Construction, Structural Steel, Mechanized Erection
Ml-Based Solar Power Generation Forecasting For Renewable Energy Integration
On a global scale, PV power generation has become a key part of the renewable energy infrastructure, where accurate forecasting is critical for grid stabilization and resource optimization. This paper presents empirical investigation on short term solar irradiance and power output forecasting methods based on machine learning using meteorological Inputs, historical generation and temporal features. We performed a comparative study of supervised and ensemble machine learning algorithms including Random Forest, Gradient Boosting, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) neural networks using data from five solar photovoltaic installations throughout different geographical locations in India across a 24-month period (January 2022–December 2023). The DatasetThe dataset consists of hourly observations with matched meteorological variables (17,520). The study found that LSTM achieved significantly lower MAPE (4.23%), followed by Gradient Boosting (5.87%) and Random Forest (6.14%). Results Validation statistical analysis of data using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R² coefficients proved the supremacy of deep learning methods. The critical analysis indicates that classical statistical forecasting methods with ensemble methods are able to get 18.4 % improvement by additionally using temporal convolutional networks (TCN). Integration of such forecasting models with smart grid systems showed promise to save 22.3% of the curtailment losses and improve renewable energy dispatch efficiency. Secondly, it builds a data-driven framework that can be scaled for solar forecasting relevant to utility-scale solar, distributed solar and microgrid settings.
Machine Learning, Solar Power Forecasting, LSTM Neural Networks, Renewable Energy Integration, Grid Stability, Photovoltaic Generation, Time Series Prediction
Impact Of Control Strategies On Infectious Disease Dynamics: A Differential Equation Modelling And Simulation Approach
Global public health is still severely hampered by infectious diseases, which highlights the necessity for efficient control and intervention measures. Understanding the mechanisms of disease transmission and assessing the efficacy of control measures prior to their practical application depend heavily on mathematical models. A compartmental differential equation-based infectious illness model that incorporates important control techniques like immunisation, therapy, and preventive measures is presented in this paper. To establish threshold conditions for disease persistence or eradication, the fundamental reproduction number is calculated. To evaluate long-term system behaviour, stability analysis of endemic and disease-free equilibria is performed. The influence of changing control settings on disease dynamics is examined by numerical simulations employing the fourth-order Runge–Kutta method. According to simulation studies, effective control measures greatly shorten the duration of outbreaks and the prevalence of infections. The results offer policymakers and public health planners important information for creating successful disease control initiatives.
Infectious disease modelling, differential equations, control strategies, stability analysis, numerical simulation.
Copyright (c) 2025 International Journal of Multidisciplinary Engineering In Current Research