International Journal of Industrial Engineering
The International Journal of Industrial Engineering (eISSN: 2456-8449) is a peer-reviewed, open-access journal dedicated to advancing knowledge in industrial and systems engineering while promoting interdisciplinary collaboration across engineering, management, operations, and applied sciences. The journal welcomes contributions in areas including, but not limited to, operations research, production planning, supply chain management, ergonomics, robotics, quality engineering, and sustainable manufacturing, as well as studies that integrate industrial engineering with computer science, data analytics, artificial intelligence, electrical engineering, electronics engineering, civil engineering, environmental studies, and business management. By fostering cross-disciplinary approaches, it provides a platform for innovative solutions that enhance efficiency, productivity, and sustainability in modern industrial systems.
Depth-Enhanced Object Identification and Interaction in Augmented Reality Using an ARKit-Based Framework
Augmented Reality (AR) has emerged as a potent technology for instantaneous object recognition and interactive representation. This research introduces an augmented reality identification and interaction framework created using Apple’s ARKit platform. The experimental implementation uses scene data and object annotations sourced from GitHub repositories related to ARKitScenes. The dataset comprises geographical mapping data, RGB image frames, depth maps, 3D scene reconstruction information, camera intrinsics, object bounding boxes, and pose estimation parameters. The system considers essential characteristics such as camera focal length, intrinsic matrix values, depth resolution, object coordinates (x, y, z), and feature descriptors derived from image frames. The proposed method incorporates feature extraction, spatial alignment, and real-time interaction components to improve object identification accuracy in augmented environments. The performance assessment indicates consistent object identification, stable tracking, and effective interaction management across diverse lighting and scene conditions. The framework emphasizes the practical capabilities of ARKit-based systems for immersive identification and interaction applications in smart environments, education, and industrial support systems.
ARKit, Apple, Object identification, Real-time interaction, Augmented reality applications
Copyright (c) 2026 Ojasvi Pattanaik, Balachandra Pattanaik, Manjula Pattnaik https://creativecommons.org/licenses/by/4.0
Cloud-Powered Industrial IoT Platform for Continuous Equipment Health Tracking and Failure Alerts
Constantly monitoring the industrial apparatus is essential to keep them in the operation mode, reduce unpredictable accidents, and improve the effectiveness of maintenance. Traditional maintenance schedules that use period reviews or on-repair services usually lead to manufacturing delays and increased cost of operation. This paper provides an IIoT cloud-based platform to be used in continuous monitoring of equipment health and generation of automatic failure signals. The system is designed with many sensors which can help to receive the necessary machine parameters including temperature, vibration, pressure, and current. The obtained information is transported through an edge gateway to a cloud system to store it, process it, and conduct real-time monitoring. The system compares the trend of the parameters against the set threshold values, to determine an anomaly and give early alarms to the maintenance officials. There is experimental evaluation of six pieces of industrial equipment whose operational evaluation shows a significant enhancement. Based on the findings, the efficiency of equipment went up to 87 and system dependability went up to 93, as compared to 68 and 72 respectively. More so, the amount of equipment devoted to downtime dropped to 28 hours per month, and the response time on maintenance was reduced to 33 minutes per month. The quantitative results prove that the offered IIoT monitoring system based on clouds is the solution that has a strong positive effect on equipment reliability, facilitates its proactive maintenance and ensures the increased productivity of the industrial sector in general.
Industrial IoT, Cloud computing, Equipment health monitoring, Real-time data analytics, Sensor networks, Operational efficiency
Copyright (c) 2026 Mohamed Uvaze Ahamed https://creativecommons.org/licenses/by/4.0
Hybrid Swin Transformer with Capsule Attention for Ceramic Tile Surface Classification
Ceramic tile surface classification is very important for quality control since it shows how various tile types have varied textures, patterns, and surface properties. To overcome the issue, a hybrid Swin Transformer with Capsule Attention architecture is created to successfully capture both global contextual data and fine-grained spatial interactions. The architecture combines hierarchical feature extraction from the Swin Transformer with dynamic routing-based capsule attention. This makes it possible to better represent features using a dual-branch design and a feature fusion method. The VxC TSG database is used for experiments. It has several tile categories, such as Agata, Berlin, Lima, Marfil, Mediterranea, Oslo, Petra, Santiago, Somport, Vega, and Venice. Each category has three classes that show different surface conditions. The suggested model has an average accuracy of 98.83%, a precision of 98.25%, a recall of 98.25%, and an F1-score of 98.25%. This shows that it works well in all areas. Also, an AUC of 97.81% shows that the difference between things quite well. The results show that using transformer-based learning with capsule attention works well for strong and accurate classification of ceramic tile surfaces.
Ceramic tile surface classification, Swin transformer, Capsule attention, Feature fusion, Texture analysis
Copyright (c) 2026 Manjula V. S, Hanadi Ahmed Elnaem, Lwanga N Kulsum https://creativecommons.org/licenses/by/4.0
Business Intelligence-Driven Analysis of Airline Passenger Satisfaction Using SAS Enterprise Miner
Business intelligence is essential for converting corporate data into meaningful insights for strategic decision-making. This research presents an advanced data mining architecture using SAS Enterprise Miner to examine customer satisfaction trends for business intelligence purposes. The experimental assessment used a publicly accessible Kaggle dataset including airline passenger service and demographic data. The dataset comprises essential factors such as Age, Flight Distance, Departure Delay, Arrival Delay, Class, Type of Travel, and several service rating criteria such as Inflight Wi-Fi Service, Seat Comfort, Food and Drink, and On-board Service, assessed on a 1–5 scale. Data preprocessing methods, such as missing value imputation, categorical encoding, and normalization, were implemented before model construction. Classification algorithms were developed to forecast passenger satisfaction levels and identify significant business factors. The model's performance was assessed using accuracy, precision, recall, and AUC metrics. The findings indicate that sophisticated data mining methodologies in SAS Enterprise Miner effectively reveal essential service quality metrics, facilitating data-informed decision-making and improved business intelligence approaches.
Data mining, Predictive analytics, Business intelligence, SAS enterprise miner, Big data
Copyright (c) 2026 Arunkumar Velayutham, Sivashanmugam Thangavel https://creativecommons.org/licenses/by/4.0
AI-Driven Self-Learning Network Management for Industrial IoT Wireless Sensor Networks Using Autoencoder and Reinforcement Learning
Industrial Wireless Sensor Networks (IWSNs) are essential to Industrial Internet of Things (IIoT) systems because they allow automation, data-driven decision-making, and monitoring in smart industrial settings. However, these networks encounter several challenges such as energy limitations, abnormal node behavior, packet loss, and unpredictable communication links. To discover these problems, this paper introduces an Artificial Intelligence (AI)-driven self-learning network management system that combines Autoencoder-based anomaly detection and Reinforcement Learning (RL)-based routing optimization. The Autoencoder algorithm learns normal network behavior patterns and identifies anomalous sensor nodes based on reconstruction error, allowing early detection of anomalous activities or faults. Using network characteristics like residual energy, link quality, and congestion level, reinforcement learning is used to dynamically choose the best routing path. This hybrid AI approach assists adaptive decision-making and nonstop learning in dynamic industrial environments. Simulation results illustrate that the proposed system enhances anomaly detection accuracy and increases network throughput compared with traditional approaches. This intelligent approach offers a scalable solution for dynamic network management in IIoT environments.
Autoencoder, Reinforcement Learning, Industrial Wireless Sensor Networks, AI-driven self-learning, Adaptive decision
Copyright (c) 2026 Suresh Babu Changalasetty, Rahaf Alhwaij, Meznah Shaail Albogami https://creativecommons.org/licenses/by/4.0
Cross-correlation based Underwater Node Estimation Technique and Concise Summary of Different Effects
To overcome the common problem, protocol complexity of conventional underwater node estimation techniques, a statistical signal processing technique was proposed as an alternative. The technique uses cross-correlation as signal processing tool and hence it is called cross-correlation based underwater node estimation technique. This technique can overcome the protocol complexities, human interaction, over cost, etc., of conventional techniques. However, to introduce the readers with different technical aspects regarding this technique, the paper is introduced. We have used mean, and standard deviation as our estimation parameter to estimate nodes. At the same time, we have illustrated a brief discussion on effect of signal length, underwater bandwidth, etc., simultaneously in this paper. The present work can boost the researches in this field.
Underwater Network; Wireless Sensor Network; Signal Processing; CHRIP
Copyright (c) 2019 https://creativecommons.org/licenses/by/4.0
Predictive Talent Management Leveraging AI for Workforce Optimization
In the era of data driven decision-making, talent management has evolved beyond traditional intuition-based approaches into a strategic imperative powered by artificial intelligence. This study proposes a robust, predictive framework that leverages machine learning algorithms and behavioral analytics to proactively manage workforce dynamics. Drawing on comprehensive multi-source data from over 1,000 employees across diverse sectors including pharmaceuticals, IT, infrastructure, and education the model integrates key indicators such as Retention Risk Score (RRS), Career Progression Index (CPI), and Skill Gap Index (SGI) to uncover actionable insights into employee engagement, performance, and attrition risk. The framework demonstrates a predictive accuracy of 92% and reveals a statistically significant inverse correlation (r = –0.84) between engagement levels and attrition likelihood. Cluster based segmentation further enables organizations to classify employees into strategic categories such as high potential, at risk, and development needed facilitating targeted HR interventions. Enhanced with sentiment analysis and real-time dashboard visualizations, this AI driven system empowers organizations to transition from reactive HR operations to proactive, evidence-based talent strategies, thereby optimizing workforce stability, growth, and competitive advantage.
Predictive Talent Management, Employee Retention Analytics, Career Progression Index (CPI), Artificial Intelligence in HR, Workforce Optimization, Sentiment Analysis.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Logistic Regression Based Decision Support System for Power Grid Operations
Ensuring power grid stability during critical operating situations requires prompt and precise choices by system dispatchers. This paper uses Logistic Regression (LR) to forecast dispatcher actions using the Power Grid Dispatcher Operations dataset from Kaggle, including 1,000 labelled scenarios that include voltage deviations, load variations, and equipment failures. Following preprocessing and feature selection, a binary LR model was developed to classify whether dispatchers will implement remedial measures. The model achieved an accuracy of 98.50%, precision of 98.02%, recall of 99%, and an F1-score of 98.50%, demonstrating robust predictive efficacy. Critical factors affecting decisions were frequency variation, reserve margins, and line loading percentages. Class-weighted loss functions and stratified sampling were used to mitigate data imbalance among action classes. The model's interpretability, together with its efficacy, endorses its use as a decision-support instrument in real-time operations. The results indicate the effectiveness of statistical learning methods in improving dispatcher situational awareness, minimizing human error, and strengthening grid resilience under high-risk situations.
Power Grid Stability, Dispatcher Decision Prediction, Logistic Regression, Grid Resilience, Smart Grid
Adaptive Load Balancing in Industrial IoT Sensor Networks Using Dynamic Spanning Tree-Based Routing
Spanning tree-based dynamic graph routing implements a topology-aware approach for facilitating adaptive load balancing in clustered Wireless Sensor Networks (WSNs). Traditional routing methods often encounter communication constraints and energy inefficiencies resulting from fixed route choices and unequal data distribution across cluster members. The proposed framework establishes a spanning tree structure that dynamically adjusts to changes in topology, energy levels, and node density. The main goal is to enhance route selection while guaranteeing equitable workload allocation across all clusters, thereby prolonging network lifespan and reducing packet latency. The approach combines dynamic edge reconfiguration with localized decision-making, enabling real-time modifications in routing pathways according to residual energy and communication traffic. Each cluster creates an energy-weighted spanning tree that is regularly updated via distributed coordination, improving scalability and fault tolerance. Experimental assessments under diverse network settings reveal enhanced throughput, decreased transmission overhead, and stable energy usage across all nodes, confirming its appropriateness for extensive WSN implementations.
Spanning Tree Routing, Load Balancing, Clustered Wireless Sensor Networks, Dynamic Graph Algorithms, Energy Optimization
Quantum Annealing for Sustainable Supply Chains to Reduce Carbon Footprint
This investigates Quantum Annealing as a game-changing strategy for dealing with complicated combinatorial optimization issues, with the goal of achieving sustainable supply chains and decreased carbon footprints. The purpose of the research is to harness the potential of Quantum Computing, notably focused on Quadratic Unconstrained Binary Optimization formulations, Ising Model, Maximum Cut Problem, and the Traveling Salesman Problem. The goal of this is to expand the use of Quantum Annealing methods in the logistics, supply chain, and environmental fields. The goal of this study is to use Quantum Annealing's powerful capability to effectively explore large solution spaces as a springboard for developing novel approaches. A quantum-powered paradigm change in supply chain optimization is made possible by the new application of Quantum Annealing to real-world sustainability concerns. This is groundbreaking since it is the first to include quantum algorithms into eco-friendly supply chain planning by going into the details of QUBO, the Ising Model, MAX-CUT, and TSP. It offers a fresh viewpoint on reducing the carbon footprint of supply chains, which in turn encourages more environmentally friendly corporate procedures. It lays the groundwork for future developments in quantum-enabled sustainability and highlights the critical role that cutting-edge technologies play in driving global supply chain environmental conservation initiatives.
Quantum Annealing, Sustainable Supply Chains, Carbon Footprint Reduction, Quadratic Unconstrained Binary Optimization, Quantum Computing, Combinatorial Optimization, Environmental Conservation
Predictive Maintenance Strategies for Smart Buildings Using IoT and Time Series Analytics
This study investigates the enhancement of predictive maintenance in smart buildings by the utilization of Internet of Things (IoT) sensors and Time Series Analytics, specifically applying Temporal Fusion Transformers (TFT). The incorporation of IoT devices facilitates the ongoing surveillance of critical parameters including temperature, humidity, vibration, and energy usage throughout diverse building systems. Utilizing TFT's capacity to identify temporal relationships, the model accurately forecasts possible equipment failures, facilitating proactive maintenance measures. The evaluation revealed that the TFT model achieved a 15% enhancement in failure prediction accuracy compared to conventional time series forecasting models such as ARIMA and LSTM. Moreover, by forecasting breakdowns 5–7 days ahead, maintenance teams might diminish emergency repair expenses by 25%, so prolonging the lifespan of essential building infrastructure by as much as 18%. The findings indicate that the integration of IoT data with sophisticated machine learning methodologies substantially improves predictive maintenance, yielding cost reductions and operational efficiency in smart buildings. The results highlight the revolutionary capability of TFT in enhancing maintenance procedures within smart environments, facilitating more sustainable building management.
Smart Buildings, Temporal Fusion Transformers, Building Management Systems, Internet of Things, Operational Efficiency
Dynamic Process Control in Smart Manufacturing Systems Using Deep Q-Networks
This research examines the use of Deep Q-Networks (DQNs) for the optimization of dynamic processes in intelligent manufacturing systems. Utilizing real-time sensor data and adaptive decision-making, DQNs optimize essential manufacturing parameters, including production rate, energy consumption, and equipment utilization, to improve overall operational efficiency. A case study demonstrates the application of a DQN-based model in a manufacturing setting, resulting in a 15% reduction in production time and a 12% drop in energy consumption relative to conventional rule-based optimization techniques. The model's adaptability to varying conditions, such as demand variations and equipment failures, was assessed by simulation, revealing a 20% enhancement in system responsiveness and an 18% increase in throughput. The findings underscore the efficacy of reinforcement learning in enhancing smart manufacturing processes, delivering real-time, data-driven insights that markedly surpass traditional optimization methods. The results indicate that incorporating DQNs into industrial systems can significantly enhance operational efficiency and resource management, hence advancing Industry 4.0.
Smart Manufacturing, Deep Q-Networks, Internet of Things, Real-Time Process Optimization, Energy Efficiency, Adaptive Control Systems
Copyright (c) 2025 M Madheswaran, V Munusami, A Selvaraj, A. G. Ramu https://creativecommons.org/licenses/by/4.0
Predictive Collision Risk Modeling in Autonomous Driving Using Random Forest and IoT Data
The safety of autonomous cars is largely contingent upon their capacity to anticipate and evade possible accidents in real time. The integration of Internet Things (IoT)-enabled sensors and communication systems allows cars to collect extensive environmental and operational data, facilitating informed decision-making. This paper presents a predicted collision risk model based on Random Forest (RF) that employs multi-sensor IoT data, including vehicle speed, acceleration, braking habits, lane position, meteorological conditions, and nearby traffic density. RF is chosen for its resilience, clarity, and capacity to manage high-dimensional, diverse datasets without succumbing to overfitting. The program evaluates dynamic driving situations to predict accident risks and facilitates preventive actions like controlled braking or lane adjustments. Experimental findings demonstrate increased prediction accuracy with little computational lag, making it appropriate for real-time implementation. The proposed approach augments Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) awareness, hence enhancing safety and efficiency in autonomous driving systems.
Autonomous Driving, Predictive Collision Risk, Random Forest, Intelligent Transportation Systems, IoT Sensor Data.
Copyright (c) 2025 Kamalakannan Machap, A Rajalingam https://creativecommons.org/licenses/by/4.0
Advanced Battery Management Algorithms for Enhanced Safety and Lifespan in Electric Vehicles
Battery systems that provide high energy density and performance, unwavering safety, and an extended operating lifetime are required due to the growing popularity of electric vehicles (EVs). Despite their efficiency, lithium-ion batteries are vulnerable to safety hazards and degradation processes brought on by deep draining, overcharging, temperature imbalances, and uneven cell ageing. To overcome these difficulties, this work explores sophisticated battery management algorithms that use improved monitoring, prediction, and control techniques. The suggested methods allow proactive defect identification, adaptive charging profiles, intelligent thermal control, and precise state-of-charge (SOC) and state-of-health (SOH) estimates by using model-based estimation, machine learning techniques, and real-time optimization. Results from simulations and experimental validation show that these algorithms can substantially decrease capacity fading, increase energy efficiency, and lessen safety risks in a variety of operating scenarios. The results demonstrate how important algorithm-driven Battery Management Systems (BMS) are to guarantee the lifespan, safety, and dependability of EV battery packs and hasten the shift to environmentally friendly transportation.
Battery Management System (BMS), Electric Vehicles (EVs), Battery Safety, Advanced Algorithms, Fault Detection
Copyright (c) 2025 Yohannes Bekuma Bakare, Kumarasamy M https://creativecommons.org/licenses/by/4.0
Long-Short-Term Memory Networks for Diabetes Disease Prediction Using Artificial Intelligence
This research investigates the application of AI-driven Long Short-Term Memory (LSTM) networks for the prediction of diabetic illness. LSTM models are well-suited for analyzing the temporal nature of diabetes-related data, enabling accurate forecasting of disease progression and associated complications. The primary objective is to develop LSTM architectures capable of capturing complex patterns in longitudinal patient data such as blood glucose levels, medication adherence, and lifestyle behaviors and to evaluate their predictive performance against traditional statistical and machine learning methods. By leveraging artificial intelligence, this study aims to deliver timely insights into the trajectory of diabetes, thereby empowering both patients and healthcare providers to make more informed decisions. These insights can facilitate the optimization of treatment strategies and enhance preventive care to reduce diabetes-related complications. The dataset utilized in this study is sourced from Data World, encompassing various aspects of diabetes prediction, classification, and healthcare outcomes. One component of the dataset indicates an equal distribution of diabetic and non-diabetic patients (5 out of 10 each). Other dataset segments highlight high BMI and elevated cholesterol levels as significant predictors of diabetes in individuals aged 60–70. Additionally, factors such as smoking, heart disease, and BMI are found to influence diabetes risk across both male and female demographic groups in separate database subsets.
Artificial Intelligence-driven, Long-Short-Term Memory Networks, Diabetes, Predictive Analysis
Copyright (c) 2025 Durai Allwin, Dhaniyasravani M https://creativecommons.org/licenses/by/4.0
Cloud-Based NVivo for Predictive Analysis in Movie Success and Audience Insights
The global film industry faces ongoing challenges in predicting audience preferences, forecasting box office performance, and optimizing marketing strategies. Traditional approaches, reliant on historical data and generalized assumptions, often lack accuracy and adaptability. This study introduces a cloud-based NVivo framework for predictive analysis, integrating structured metrics such as box office data with unstructured sources including social media comments, reviews, and audience feedback. The methodology employs qualitative coding, sentiment analysis, and machine learning techniques to generate real-time insights into audience behavior and film performance. Results demonstrate that NVivo’s predictive analytics enhance the accuracy of success forecasting, provide demographic-specific insights, and support proactive risk assessment. The system further facilitates tailored marketing strategies and content recommendations, enabling studios and marketers to respond dynamically to evolving audience sentiments. This research highlights the transformative potential of cloud-based qualitative analytics in driving data-informed decision-making within the entertainment industry.
Cloud-Based NVivo, Predictive Analysis, Movie Success, Audience Insights, Sentiment Classification
Copyright (c) 2025 V Sujatha, Gayathri Rajkumar https://creativecommons.org/licenses/by/4.0
IoT-Enabled Green Roof Management with RNN-Based Dynamic Environmental Control
This research presents a dynamic environmental management system for urban landscapes that leverages Recurrent Neural Networks (RNNs) in the context of IoT-enabled green roof management. Green roofs play a pivotal role in promoting urban sustainability by improving air quality, mitigating urban heat island effects, and enhancing energy efficiency. The proposed system integrates a network of environmental sensors—measuring temperature, humidity, light intensity, soil moisture, and air quality—to continuously monitor and optimize the performance of green roofs. The collected sensor data is processed using an RNN model, which is particularly well-suited for this application due to its ability to capture temporal dependencies and trends in sequential data. By forecasting environmental conditions, the RNN provides insights into the operational health and effectiveness of green roofs. Moreover, the system supports real-time decision-making by offering adaptive recommendations for controlling ventilation, lighting, and irrigation systems. This dynamic feedback mechanism not only reduces energy consumption but also strengthens the overall sustainability of urban environments. Experimental evaluations demonstrate that the RNN-based approach achieves superior accuracy, precision, and recall compared to conventional models. These findings highlight the potential of combining IoT technologies with advanced machine learning to optimize urban green infrastructure. The study underscores the broader significance of such intelligent systems in advancing sustainable urban development and in enhancing the resilience of cities to environmental challenges.
Urban Sustainability, Environmental Monitoring, Recurrent Neural Network (RNN), Internet of Things (IoT), Green Roofs, Energy Efficiency
Copyright (c) 2025 N. L. Venkataraman, S Sumithra, S Sureshkumar, R Purushothaman, K Kukulavani, V Gowri https://creativecommons.org/licenses/by/4.0
Data-Driven Soil Fertility Classification Using IoT Sensing and Logistic Regression Model
The evaluation of soil fertility is crucial for maximizing agricultural output and promoting sustainable crop management. Traditional approaches are laborious, expensive, and inadequate for real-time surveillance. This research introduces a data-driven classification method for soil fertility that integrates Internet of Things (IoT) sensors with a Logistic Regression (LR) model. IoT-enabled sensors gather essential soil factors, including moisture, temperature, pH, and nutrient levels, relaying the information to a cloud-based processing unit. The dataset is preprocessed, and features are extracted before training the LR classifier to classify soil fertility levels. The model is selected for its efficacy, clarity, and appropriateness for both binary and multiclass classification. Experimental validation using real-time field data shows significant accuracy and little processing delay, facilitating prompt and informed decision-making. The proposed approach enhances precision agriculture by delivering actionable information, minimizing resource waste, and fostering sustainable agricultural practices.
Soil Fertility, IoT Sensing, Logistic Regression, Precision Agriculture, Smart Farming, Data-Driven Classification, Decision Support System.
Copyright (c) 2024 Aaron Kevin Cameron Theoderaj https://creativecommons.org/licenses/by/4.0
Application of AI and XGBoost Algorithm in Sperm Analysis for Improved Diagnosis of Male Infertility
This study explores the transformative potential of artificial intelligence, particularly the XGBoost algorithm, in enhancing sperm analysis for male infertility diagnosis. Its primary aim is to identify and characterize sperm abnormalities to enable timely interventions and personalized treatment, with a focus on improving diagnostic accuracy and effectiveness. The study evaluates how AI and XGBoost can analyze large datasets of sperm characteristics, uncovering subtle patterns indicative of infertility. The overarching objective is to develop an automated system capable of accurately classifying sperm samples based on criteria such as concentration, motility, and morphology. By leveraging machine learning and the computational power of AI, this research seeks to advance male infertility diagnosis, improve understanding of the complex factors contributing to infertility, and ultimately support more accurate diagnoses and better patient outcomes. Performance results from the MHSMA system show a Recall of 75.68, G-mean of 81.32, Precision of 82.54, and AUC of 82.21, demonstrating its promising diagnostic capability.
Artificial Intelligence, XGBoost Algorithm, Male Infertility, Sperm Analysis, Diagnosis.
Copyright (c) 2024 Ravi Kumar P, Herbert Raj P https://creativecommons.org/licenses/by/4.0
Integrating ClaimBuster with Cloud Computing for Automated Fake News Detection
The rapid spread of fake news across digital platforms poses a serious threat to information integrity and public trust. Traditional fact-checking approaches are often limited by scalability and timeliness. This paper presents the integration of ClaimBuster, an automated fact-checking system, with cloud computing infrastructure to provide a scalable, accurate, and efficient solution for real-time misinformation detection. The proposed system integrates machine learning algorithms and Natural Language Processing (NLP) techniques to classify claims as true or false, supported by cloud-enabled elastic computing resources for handling high-volume, high-velocity data streams. Experimental evaluation demonstrates strong performance in terms of accuracy, precision, recall, and F1-score, with low false positive rates, confirming its robustness in real-world applications. Furthermore, advanced visualization tools, gamification features, and partnerships with media, academic, and governmental institutions enhance user engagement and the overall impact of the system. By prioritizing scalability, adaptability, privacy, and ethics, ClaimBuster within cloud computing offers a comprehensive framework for combating the global challenge of disinformation.
Fake News Detection, ClaimBuster, Cloud Computing, Scalable Systems, Misinformation
Copyright (c) 2024 Thirumoorthy Arumugam, P Senthilraja, A Gnanabaskaran https://creativecommons.org/licenses/by/4.0
Personalized Virtual Field Trips in Education using Random Forest Algorithm and IoT
The rapid growth of Internet of Things (IoT) technology has significantly transformed multiple sectors, including education, by enabling innovative solutions for personalized learning. One such application is Virtual Field Trips (VFTs), which have emerged as a powerful teaching resource, offering students immersive experiences of real-world environments while remaining within their classrooms. By integrating IoT devices such as sensors, cameras, and augmented reality (AR) systems, VFTs can be tailored to individual learning preferences, thereby creating a more interactive and engaging experience. To effectively process the vast amount of data collected from these devices, Machine Learning (ML) techniques play a pivotal role. In particular, the Random Forest algorithm, known for its robustness and ability to handle complex data structures, enhances personalization by analyzing student behaviors, preferences, and performance metrics. This capability allows the system to generate reliable predictions and adaptive recommendations. The study examines the design and implementation of a customized VFT system powered by IoT and Random Forest-based ML, with the primary objective of meeting diverse learner needs by dynamically adjusting content and activities. Such an approach not only enriches the learning experience but also fosters greater student engagement, deeper understanding, and improved educational outcomes.
Personalized Learning, Virtual Field Trips, Adaptive Education, Educational Technology, Student Engagement.
Copyright (c) 2024 Pramod Pandey, Gnana Rajesh D https://creativecommons.org/licenses/by/4.0
Cloud-Based Early Detection of Parkinson’s Disease Using AI Tools in Amazon Web Services SageMaker
Parkinson’s disease is a progressive neurological disorder that remains difficult to diagnose at early stages due to subtle symptom onset and reliance on subjective clinical assessments. Early identification is crucial to improving patient outcomes, reducing long-term healthcare costs, and enabling timely therapeutic interventions. This study proposes a cloud-based diagnostic framework leveraging Amazon Web Services (AWS) SageMaker to integrate diverse patient datasets including clinical records, speech samples, handwriting analysis, and wearable sensor data—for predictive modeling. The architecture incorporates an Extract, Transform, and Load (ETL) module for preprocessing, supervised machine learning models for training and validation, and a feedback loop for continuous refinement. Through interactive dashboards, healthcare professionals can access predictions and risk assessments in real time, supporting informed decision-making. Security and compliance are ensured through HIPAA-compliant encryption and role-based access control, while scalability is achieved via SageMaker’s managed infrastructure. Quantitative evaluation confirms high predictive sensitivity and accuracy, with heatmap and bar chart analyses identifying high-risk individuals and time-series tracking demonstrating effective progression monitoring, establishing the framework’s reliability in early Parkinson’s detection.
Early Detection, Parkinson's Disease, AI Tools, Amazon Web Services SageMaker, Cloud Computing
Copyright (c) 2024 M Muthulekshmi, S Sujatha https://creativecommons.org/licenses/by/4.0
Gaussian Processes for Modeling Drug Response in Cancer Treatment
The main objective is to get a better knowledge of how general practitioners can accurately forecast the effectiveness of cancer drugs and the results for their patients. This research intends to tackle the difficulties of personalized cancer treatment by making use of the adaptability and uncertainty quantification that Primary Care Physicians (PCPs) provide via extensive analysis. The goal is to provide the groundwork for effective, individualized treatment plans that maximize therapeutic treatments with few side effects. The objective is to create prediction models that can efficiently anticipate how a patient will react to a therapy by combining data that is unique to everyone, such as their genetic makeup and clinical characteristics. This study aims to show that GP-based methods may optimize cancer treatments and might be useful in the clinic by conducting extensive experiments and validation. Implications for better patient outcomes and treatment decision-making stem from this investigation's contribution to cancer precision medicine paradigm advancements. Data from six patients' samples for Drugs A, B, and C were analyzed in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Dosage ranges include 50–85 mg, response time is 5.8–8.1 days, side effect severity is 1.8–4.8 days, and treatment lasts 8–15 weeks.
Gaussian Processes, Cancer Treatment, Drug Response Modeling, Precision Medicine, Personalized Therapy
Copyright (c) 2024 Bavanipriya V, Abirami M https://creativecommons.org/licenses/by/4.0
IBM Watson Studio for Crude Oil Price Forecasting Using Cloud-Based Techniques
Economic planning, risk management, and investment strategies depend on crude oil price predictions. Advanced analytical capabilities in IBM Watson Studio, a cloud-based data science platform, enable reliable forecasting models. The goal is to improve crude oil price forecasts using IBM Watson Studio's machine learning. The goal is to analyze historical pricing patterns, market indicators, and geopolitical effects using cloud-based data processing, predictive modelling, and real-time analytics. We want to create a scalable, automated forecasting system to enhance energy decision-making. IBM Watson Studio uses machine learning and deep learning for data intake, feature engineering, and model training. Scalable cloud deployment allows large-scale data processing and real-time model upgrades. Automated data pipelines reduce human involvement and improve predictions. Predictive analytics improve price trend detection, revealing market variations. Deep learning models, real-time data integration, and adaptive forecasting may improve. Cloud-based forecasting supports proactive market risk mitigation and resource allocation in energy trading, financial planning, and policymaking.
Crude Oil Price Forecasting, IBM Watson Studio, Cloud-Based Techniques, Machine Learning Models, Energy Market Analytics.
Copyright (c) 2024 D S Deepika, Raja Thimmarayan, S Durga Devi, V Vidya Lakshmi, K Jeevitha, Monisha R https://creativecommons.org/licenses/by/4.0
Enhancing Urban Forest Sustainability Using IoT and Gradient Boosting for Proactive Tree Care and Environmental Management
Urban forests are essential for augmenting biodiversity, alleviating climate change, and boosting the quality of urban life. Challenges like growing urbanization and environmental deterioration need proactive and sustainable management techniques. This research presents an Internet of Things (IoT)-enabled system combined with Gradient Boosting for real-time monitoring and predictive analysis of urban forests. IoT sensors perpetually gather data on essential elements such soil moisture, temperature, air quality, and markers of tree health. The Gradient Boosting technique is used to examine these data streams, demonstrating proficiency in predictive modeling and managing intricate environmental information. Technology provides early identification of tree stress, forecasts health trends, and supports data-informed actions to enhance urban forest management. Initial findings indicate the system's capacity to optimize resource distribution, decrease maintenance expenses, and bolster urban forest resilience. This method utilizes IoT and sophisticated AI to promote sustainable urban development and enhance environmental health. It emphasizes the revolutionary potential of combining IoT with Gradient Boosting to guarantee urban forest sustainability and proactive environmental management.
Environmental Management, Gradient Boosting Model, urban forestry, Tree Health, Internet of Things (IoT).
Copyright (c) 2024 A. V Mayakkannan https://creativecommons.org/licenses/by/4.0
Developing Cloud-Based Sales Forecasting Solutions with Integrated Zoho CRM for Enhanced Business Insights
Cloud-based sales forecasting solutions with Zoho CRM improve corporate insights via data analytics and automation. However, data inconsistency, integration complexity, and real-time processing affect predicting accuracy. Advanced data cleaning and preprocessing are needed to improve predictive model performance due to data quality variations across numerous sources. Technical concerns include handling huge datasets and automating process execution to synchronize Zoho CRM and cloud-based analytics systems. Due to market volatility, previous sales data may be inaccurate, and prediction algorithms may struggle to adapt to quickly changing customer behavior. To anticipate accurately, machine learning algorithms must be refined, and computing resource optimization is necessary for high-volume transactions. Automation using predictive analytics dashboards, AI-driven forecasting models, and anomaly detection for market fluctuations may improve. Decision-making may be improved via scalable cloud infrastructure and third-party integration. Optimizing a cloud-based sales forecasting system improves revenue estimates, inventory management, and strategic planning. Zoho CRM integration with cloud analytics systems improves data-driven company choices, boosting growth and competitiveness in dynamic markets.
Cloud-based sales forecasting, Zoho CRM integration, business insights, predictive analytics, data-driven decision-making.
Copyright (c) 2024 Nelson Kennedy Babu C, A Deepa, S Sankar, B Prasad, Haritha V https://creativecommons.org/licenses/by/4.0
Effective Workflow Automation and Data Mining Models Using KNIME Analytics Platform
Process automation and data mining are used more to improve decision-making and operations. KNIME Analytics Platform is a powerful, open-source tool for data-driven process design, execution, and optimization. Using KNIME for process automation and sophisticated data mining is the goal. An automated platform for machine learning models, data processing, and predictive analytics simplifies difficult analytical operations. Data processing efficiency, model correctness, and seamless integration of numerous data sources for intelligent analysis are the goals. KNIME allows modular workflow design to automate repetitive processes while assuring repeatability and scalability. A large library of prebuilt nodes facilitates classification, clustering, and anomaly detection in data mining. Automated preprocessing, model training, and assessment of real-time data pipelines enable analytical activities with minimum human involvement. The platform's big data foundation and cloud service compatibility boosts computational efficiency. Continuous model refining improves prediction accuracy and pattern recognition with adaptive learning. This method promotes data-driven decision-making across sectors, process automation, and machine learning in complex analytical domains.
Workflow Automation, Data Mining, KNIME Analytics, Decision-Making, Machine Learning
Copyright (c) 2024 Subalya S, N Jayanthi, R Tamilmaran, T Rekha Kiran Kumar https://creativecommons.org/licenses/by/4.0
Android Spy Camera System to Enhance Security Using Super Resolution Images
. Studied the possibility of a "transplantation attack," in which malicious apps could take privacy-harming images to spy on users without the Android Application Programming Interface (API) auditing being aware of it, based on the observation that spy-on-users attack by calling Android API would be detected out. Typically, applications will need to make API calls to the Android Camera Service, a media server process, to snap a photo. To carry out a transplantation attack, a malicious program must copy the code responsible for taking photos from the media server process and then use these codes to take photos in its own address spaces, by passing the need for inter-process communication (IPC). This allows one to avoid being audited by the API. The results of studies show that the transplanting assault is real. The spy-on-user assault is also made more covert by the transplanting attack. Found that approximately half of the 69 Smartphones evaluated (produced by eight manufacturers) were vulnerable to the transplanting attack developed. In addition, the assault may hide from seven different antivirus scanners and the Android Device Administration APIs, which are utilized for enterprise-level mobile device administration. The transplanting attack prompted us to find a minor flaw in Android's security architecture and code using a super-resolution algorithm. The results show the scalability scores for Android versions
Android, Spy on Users, Transportation Attacks, Android Camera Services, Super Resolution Algorithm
Copyright (c) 2023 T Sivakumar, K Mahalakshmi, P Shobha Rani, B Maruthu Kannan https://creativecommons.org/licenses/by/4.0
Natural Disaster Prediction with Cloud Integration for Enhancing Validation Using Gradient Boosting Algorithm
Annually, natural catastrophes inflict heavy tolls in terms of human lives lost, property destroyed, and economies hit hard. Information and communications technology (ICT) enables the executions of diverse models that handle different parts of disaster management, while geospatial scientists work to reduce or control these risks via computer modelling of these complicated occurrences. Complex natural hazard models, requiring large-scale computing resources, intense data, and concurrent access, make it impossible to execute these models using standard ICT foundations in a timely way. Offering natural hazard modelling systems as end service is now more feasible than ever before, because cloud computing's almost infinite computing, storage, and networking power, which can handle these issues. Researchers still have a way to go before they can fully embrace and put Cloud Computing technology to use in disaster response. Therefore, this paper compiles all these difficulties, discusses recent trends in the field, and lays out a theoretical framework for a Cloud-based solution that makes use of the Gradient Boosting algorithm to improve the validation of systems for modelling and managing natural hazards. These systems would make use of Cloud infrastructures in tandem with other technologies, such as IoT network, fog, and edge computing. Result shows that 90% of accuracy and less prediction time compared to training time of the dataset.
Natural Hazard, Cloud Computing, Geospatial Science, Disaster Management and Gradient Boosting Algorithm
Copyright (c) 2023 MS Srikanth, S Vivek.Sharma, Raghavendra Vijay Patil, Sagar Ramesh Pujar https://creativecommons.org/licenses/by/4.0
Biometric Fingerprint ATM System to Enhance Security Using Minutiae-Based Algorithm
One alternative to using a password for authentication is to use biometric information. Fingerprint-based identification is among the most created and reliable biometric techniques. There are several benefits associated with selecting Biometric Fingerprint automated teller machines (ATM) System for minutiae -based security, the main ones being increased authentication precision, strengthened security protocols, and a dependable and easy-to-use user interface. The main objective of an ATM system that uses biometric fingerprints and a minutiae-based algorithm is to improve security and provide users of ATMs with a reliable way to authenticate themselves. A high-resolution fingerprint scanner is used to capture fingerprint images now of the transactions at the ATM. When it comes to protecting bank clients from harm, security measures may be important. When discussing vulnerabilities and causes in a court of law, these precautions are crucial. To provide their consumer with a risk-free banking experience, banks should adhere to certain criteria. This paper examines ATMs, the primary target of thieves, because of the easy access to the cash they provide, as well as the vulnerabilities and rising tide of illegal activity at ATMs. Banking security may be improved using biometric measures for both customers and employees. While the real card user is unable to make purchases, it also offers a fingerprint identification mechanism for nominees to use in their stead. The result shows 90% accuracy and 94% security assurance.
: Security, Automatic Teller Machine, Biometric Fingerprint, Verification, E-Banking, Crime and Minutiae Based Algorithm
Copyright (c) 2023 C Malarvizhi, P Dass, P Karthikeyani, A Sasikar https://creativecommons.org/licenses/by/4.0