ComputeX - Journal of Emerging Technology & Applied Science
type: channel
description: ComputeX - Journal of Emerging Technology & Applied Science is a peer-reviewed journal dedicated to cutting-edge research in computing, engineering, and applied sciences. It highlights the latest trends in emerging technologies, from artificial intelligence and data science to robotics and digital innovation. The journal provides a platform for researchers and professionals to share practical insights and scientific advancements. By bridging theory with real-world applications, ComputeX plays a vital role in advancing technology-driven solutions. It encourages interdisciplinary research that addresses both academic and industrial challenges.
publisher: RESEARCH JOURNALS ONLINE (SMC-PRIVATE) LIMITED
language: en
publication name: ComputeX - Journal of Emerging Technology & Applied Science
issn: 3106-7158
items: Role of GIS Technology, Spatial Data Analysis, and Remote Sensing in Urban Planning
items: Mathematical Modeling of Epidemic Disease Spread Considering Population Density, Vaccination Rate, and Human Mobility Patterns
items: Design of Energy-Efficient Wireless Communication Networks Using IoT-Based Traffic Management
items: Data Science Applications in Environmental Monitoring Using Remote Sensing Data, Machine Learning Algorithms, and IoT-Based Sensors
items: Role of Cloud Computing, Data Storage Scalability, and Distributed Computing Frameworks in Improving Big Data Processing Efficiency
items: Intelligent Workflow Management Systems in Educational Institutions
items: Digital Twin Models for Simulation and Optimization of Processes
items: AI-Assisted Software Testing and Bug Prediction
items: Agile Methodologies for AI-Driven Application Development
items: Attention Based Multimodal Sensor Fusion for Fall Detection Using Wearable Time-Series Data
items: Robotics-Assisted Automation for Manufacturing Sector Efficiency
items: Solar‑Powered Autonomous Drone System for Precision Agriculture
items: Design and Simulation of Energy‑Efficient Electric Vehicle Charging Stations
items: Deep Learning Algorithms for Predictive Maintenance in Industrial Machinery
items: AI‑Based Traffic Flow Prediction Models Using Real-Time Data
items: Socially Intelligent Robots for Healthcare and Assistive Applications
items: Reinforcement Learning for Autonomous Navigation in Dynamic Environments
items: Safe and Explainable AI Techniques to Human-Robot Collaboration
items: AI Driven Robotic Manipulation Using Multi Sensor Fusion
items: Vision language Models of General Purpose Robot Control
items: Use of Chatbots in Customer Service: A Technological Review
items: Role of Digital Innovation in Achieving Sustainable Development Goals (SDGs)
items: Machine Learning Applications in Predicting Climate Change Patterns
items: Green Computing: Reducing Carbon Footprints Through Energy-Efficient Technologies
items: Biometric Authentication Systems: Privacy Challenges and Technological Advances

Role of GIS Technology, Spatial Data Analysis, and Remote Sensing in Urban Planning
type: item
description: This study examines the application of Geographic Information Systems (GIS), spatial data analysis, and remote sensing in urban planning, using Faisalabad, Pakistan, as a case study. The research aims to analyze urban land-use changes and spatial expansion from 2000 to 2023, assess relationships between population density, land-use patterns, and infrastructure, and highlight the role of geospatial frameworks in sustainable urban development. Secondary data were sourced from NASA Landsat and European Space Agency Sentinel programs, complemented by population and transportation datasets. GIS processing and spatial analysis were conducted using QGIS and Python libraries, including GeoPandas, Rasterio, and PySAL. Land-use classification was performed using the Maximum Likelihood method, while NDBI and NDVI indices were used for time-series urbanization analysis. Results indicate that approximately 38.4% of urban fringe agricultural land was converted to built-up areas, with 74.2% of development occurring within 1.5 km of major roads, reflecting corridor-based growth. Spatial clustering identified seven significant high-density population zones. The findings demonstrate that GIS and remote sensing provide effective tools for monitoring urban growth and support evidence-based, sustainable planning in rapidly expanding cities.
subject: Articles
  topic: section
subject: GIS
  topic: keywords
subject: remote sensing
  topic: keywords
subject: urban planning
  topic: keywords
subject: urban land-use change
  topic: keywords
subject: Faisalabad Pakistan
  topic: keywords
subject: spatial analysis
  topic: keywords
subject: urban expansion
  topic: keywords
subject: overlay analysis
  topic: keywords
subject: buffer analysis
  topic: keywords
subject: spatial clustering
  topic: keywords
subject: sustainable development
  topic: keywords
subject: Landsat
  topic: keywords
creator: Muhammad Saleem Ashraf
creator: Hamza Khan
rights: Copyright (c) 2026 Muhammad Saleem Ashraf, Hamza Khan https://creativecommons.org/licenses/by/4.0
date: 2026-03-21
publication date: 2026-03-21
starting page: 49
ending page: 58

Mathematical Modeling of Epidemic Disease Spread Considering Population Density, Vaccination Rate, and Human Mobility Patterns
type: item
description: This study presents a quantitative mathematical model to analyze the spread of epidemic diseases in Multan, Pakistan, by integrating population density, vaccination rate, and human mobility within an extended Susceptible-Exposed-Infected-Recovered (SEIR) framework. The model aims to simulate disease dynamics under varying epidemiological scenarios, assess the impact of population density and mobility on transmission, evaluate the effectiveness of vaccination coverage, and identify key influencing parameters through sensitivity analysis. Secondary data were obtained from the Pakistan Bureau of Statistics, World Health Organization (WHO), and demographic and transportation databases. The enhanced SEIR model incorporates a mobility-scaling parameter and a vaccination-adjusted force of infection, implemented using Python libraries such as SciPy and Matplotlib. Results indicate that population density significantly influences the basic reproduction number (R₀), increasing from 1.8 in low-density settings to 3.4 in observed urban conditions. High vaccination coverage (≥70%) effectively reduces epidemic peaks and accelerates herd immunity, while mobility restrictions (50% reduction) lower cumulative infections by 38% over 180 days. Sensitivity analysis identifies transmission rate (β) and vaccination rate (ν) as the most critical parameters. The findings highlight the importance of integrated strategies combining targeted vaccination, mobility control, and density-aware interventions to effectively manage epidemics in densely populated urban areas.
subject: Articles
  topic: section
subject: SEIR model
  topic: keywords
subject: epidemic modeling
  topic: keywords
subject: population density
  topic: keywords
subject: vaccination rate
  topic: keywords
subject: human mobility
  topic: keywords
subject: Multan Pakistan
  topic: keywords
subject: disease transmission
  topic: keywords
subject: mathematical epidemiology
  topic: keywords
subject: sensitivity analysis
  topic: keywords
subject: herd immunity
  topic: keywords
creator: Muhammad Adeel Hassan
creator: Faizan Akram
rights: Copyright (c) 2026 Muhammad Adeel Hassan, Faizan Akram https://creativecommons.org/licenses/by/4.0
date: 2026-03-08
publication date: 2026-03-08
starting page: 39
ending page: 48

Design of Energy-Efficient Wireless Communication Networks Using IoT-Based Traffic Management
type: item
description: Rapid urbanization has intensified traffic congestion in major cities, leading to increased fuel consumption, air pollution, and economic losses. Traditional traffic control systems, based on fixed-cycle signals, are ineffective in handling dynamic urban traffic patterns. This study proposes an IoT-based Energy-Efficient Wireless Network (IoT-EWN) for smart traffic management in Peshawar, Pakistan. The proposed system integrates IoT sensor nodes, wireless relay nodes, and cloud-enabled gateways to monitor real-time traffic density, dynamically adjust signal timings, and transmit data through energy-efficient multi-hop communication. Key components include a Duty-Cycle Adaptive MAC (DCA-MAC) protocol, a fuzzy logic-based signal control algorithm, and an energy-aware routing protocol (GEAR-T). The system was evaluated through simulations using MATLAB R2023b and NS-3 (v3.38) over a 4 km × 4 km urban grid with 24 intersections. Results show that IoT-EWN reduces network energy consumption by 47.3% compared to traditional networks and 28.6% compared to standard IoT systems. It also achieves a packet delivery ratio of 96.8%, latency of 23.4 ms, and throughput of 4.87 Mbps. These findings demonstrate that the proposed architecture significantly improves energy efficiency, reliability, and real-time responsiveness, making it a practical solution for smart traffic management in developing urban environments.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: energy-saving wireless networks
  topic: keywords
subject: traffic management
  topic: keywords
subject: MATLAB simulation
  topic: keywords
subject: NS-3
  topic: keywords
subject: CA-MAC protocol
  topic: keywords
subject: Peshawar
  topic: keywords
subject: traffic in cities
  topic: keywords
subject: delivery ratio of packets
  topic: keywords
subject: network throughput
  topic: keywords
creator: Ayesha Perveen
creator: Zubair Shakeel Khan
rights: Copyright (c) 2026 Ayesha Perveen, Zubair Shakeel Khan https://creativecommons.org/licenses/by/4.0
date: 2026-02-23
publication date: 2026-02-23
starting page: 26
ending page: 38

Data Science Applications in Environmental Monitoring Using Remote Sensing Data, Machine Learning Algorithms, and IoT-Based Sensors
type: item
description: This study explores the use of advanced information technology methods—satellite remote sensing, machine learning, and Internet of Things (IoT)-based sensors—for environmental monitoring in Lahore, Pakistan. The objectives were to collect and preprocess multi-source environmental data, apply machine learning models (Random Forest, Support Vector Machine, and Artificial Neural Networks) for air quality classification and land surface temperature (LST) prediction, and evaluate their performance using standard metrics. Data were obtained from USGS and ESA satellite platforms, IoT-based air quality sensors, and the Pakistan Meteorological Department. Preprocessing included data cleaning, normalization, and geospatial feature extraction using Python tools. Results show that Random Forest performed best in air quality classification, achieving 91.4% accuracy, while the ANN model provided the most accurate LST predictions with an RMSE of 1.84°C. Key contributing features included NDVI, PM2.5 concentration, and land use classification. The findings demonstrate that integrating remote sensing, machine learning, and IoT technologies provides a reliable and scalable framework for real-time urban environmental monitoring, with important implications for public health and environmental policy in Lahore and similar urban regions.
subject: Articles
  topic: section
subject: remote sensing
  topic: keywords
subject: machine learning
  topic: keywords
subject: IoT sensors
  topic: keywords
subject: environmental monitoring
  topic: keywords
subject: air quality
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: neural networks
  topic: keywords
subject: Lahore Pakistan
  topic: keywords
subject: land surface temperature
  topic: keywords
subject: NDVI
  topic: keywords
subject: urban heat island
  topic: keywords
creator: Nida Qureshi
creator: Muhammad Imran Siddiqui
creator: Shiza Malik
rights: Copyright (c) 2026 Nida Qureshi, Muhammad Imran Siddiqui, Shiza Malik https://creativecommons.org/licenses/by/4.0
date: 2026-02-07
publication date: 2026-02-07
starting page: 15
ending page: 25

Role of Cloud Computing, Data Storage Scalability, and Distributed Computing Frameworks in Improving Big Data Processing Efficiency
type: item
description: The rapid growth of digitized data in modern technological environments has made efficient big data processing a critical priority for organizations. As industries increasingly rely on data-driven decision-making, the role of enabling technologies such as cloud computing, data storage scalability, and distributed computing frameworks has become central. However, empirical evidence examining their combined impact in developing countries, particularly Pakistan, remains limited. This study addresses this gap by analyzing the influence of these technologies on big data processing performance within Lahore’s IT sector. A quantitative survey was conducted using a structured questionnaire with a five-point Likert scale, collecting data from 175 IT professionals, software developers, and data engineers. Data analysis was performed using IBM SPSS Statistics for descriptive analysis and reliability testing (Cronbach’s Alpha), alongside SmartPLS for Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that all three factors significantly and positively impact big data processing performance. Cloud computing emerged as the strongest predictor (β = 0.389, p < 0.001), followed by data storage scalability (β = 0.312, p < 0.001) and distributed computing (β = 0.274, p < 0.01). Reliability and validity measures confirmed strong model consistency, while the model explained 61.4% of variance (R² = 0.614). These results highlight the strategic importance of adopting scalable and cloud-based infrastructures to enhance big data capabilities in Pakistan’s emerging IT sector.
subject: Articles
  topic: section
subject: Efficiency of Big Data Processing
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Scalability of Data storage
  topic: keywords
subject: Distributed computing frameworks
  topic: keywords
subject: Structural Equation modeling
  topic: keywords
subject: Lahore
  topic: keywords
subject: pakistan
  topic: keywords
subject: PLS-SEM
  topic: keywords
creator: Hina Ashraf
creator: Ali Raza Shah
creator: Farooq Asghar
rights: Copyright (c) 2026 Hina Ashraf, Ali Raza Shah, Farooq Asghar https://creativecommons.org/licenses/by/4.0
date: 2026-01-13
publication date: 2026-01-13
starting page: 01
ending page: 14

Intelligent Workflow Management Systems in Educational Institutions
type: item
description: The high rate of digitalization of schools and colleges has heightened the difficulty in the administration, scholarly, and business processes. Conventional workflow management solutions are usually not flexible, cannot make real-time decisions, and cannot be scaled. Intelligent Workflow Management Systems (IWfM), which is an artificial intelligence (AI), machine learning (ML), and data analytics and automation technologies, have innovative solutions to these challenges. This paper explores the role, design, and the contribution of the intelligent workflow management systems to the institutions of learning by concentrating on process optimization, decision support, efficiency improvement, as well as the improvement of the performance of the institution. The research is conducted following a mixed method approach of research by examining the contribution of IWMS to better academic administration, faculty workload management, student services, and institutional governance. This study indicates that IWMS are very effective in improving operational efficiency, lowering processing delays, enhancing transparency, and making data-driven decisions in learning institutions. The paper concludes that smart workflow systems are paramount when it comes to attaining sustainable digital transformation in the field of education.
subject: Articles
  topic: section
subject: Intelligent workflow management
  topic: keywords
subject: learning institutions
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: the digital transformation
  topic: keywords
subject: automation
  topic: keywords
subject: decision support systems
  topic: keywords
creator: Saba Syed
rights: Copyright (c) 2025 Saba Syed https://creativecommons.org/licenses/by/4.0
date: 2025-12-08
publication date: 2025-12-08
starting page: 39
ending page: 48

Digital Twin Models for Simulation and Optimization of Processes
type: item
description: Digital twin models represent a novel technology of simulating, monitoring and optimization of complex processes in many industries. Digital twin is a virtual image of an actual world system, which has capabilities to combine real-time data, predictive analytics, as well as streamline the processes. Digital twins enable companies to test and anticipate failure, enhance the effectiveness of operations and connect the real world with the digital world without disrupting the operation in real life. This paper will address how digital twins models are used in the process simulation and optimization processes, and how they can be applied to predictive maintenance, resource distribution and decision support. Through the aid of a mixed-method approach, the paper reviews case studies and empirical evidence to find out the effectiveness of digital twins in the process of improvement. The findings reveal that the digital twin models have the potential to substantially increase the visibility of the processes, reduce the cost of operation and also increase the accuracy of the decisions. The implementation issues, including the data integration, computational requirements, and scalability of the system, are also taken into consideration in the research and provide the insight into the best practices in the implementation of the digital twin technology.
subject: Articles
  topic: section
subject: Query words Digital twin
  topic: keywords
subject: process simulation
  topic: keywords
subject: process optimization
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: operational efficiency
  topic: keywords
subject: decision support
  topic: keywords
creator: Daniyal Zaheer
rights: Copyright (c) 2025 Daniyal Zaheer https://creativecommons.org/licenses/by/4.0
date: 2025-11-22
publication date: 2025-11-22
starting page: 28
ending page: 38

AI-Assisted Software Testing and Bug Prediction
type: item
description: The technology of AI has significantly transformed the manner in which software engineering is performed particularly in the field of software testing as well as bug prediction. The conventional software testing focuses on manual testing and autotesting programs based on rules, which is not only time-consuming, but also prone to errors, and could not test large-sized software systems with constant changes. It introduces AI-assisted software testing that is based on machine learning, deep learning, and data-driven algorithms to generate test cases automatically, prioritize the testing process, and predict defects before executing the code in the software. Bug prediction models rely on the historical software measures, code complexity measures, and the execution data to anticipate defect-prone parts at the early phases of the software development lifecycle. AI-assisted software testing techniques, bug prediction techniques, the results of the research, challenges, and implications are critically examined in this research article. The paper places the stress of how AI can be used to enhance the quality of their software, reduce the cost of software development, and provide a continuous integration and delivery platform.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Software testing
  topic: keywords
subject: Bug prediction
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: quality assurance
  topic: keywords
subject: test automation
  topic: keywords
subject: software engineering
  topic: keywords
creator: Dur-E- Adan
rights: Copyright (c) 2025 Dur-E- Adan https://creativecommons.org/licenses/by/4.0
date: 2025-11-13
publication date: 2025-11-13
starting page: 20
ending page: 27

Agile Methodologies for AI-Driven Application Development
type: item
description: Agile methodologies have indeed become a widely adopted approach in the modern world of software engineering for its flexibility and iterative development / customer orient approach. With the advent of artificial intelligence (AI) and artificial intelligence-based applications, the traditional methods of software development may be too static to keep pace with the dynamic requirements, complicated workflow and continuous learning capabilities of such an application. Integrating Agile principles into the application development of AI are also a designed structure and adaptability that enables rapid prototyping, modifying a gradual enhancement of the models, develop through collaboration, and constant incorporation of a feedback. This research article is all about the role of Agile methodologies in the development of AI applications, some of the best practices to consider, challenges and strategies to implement the iterative AI applications workflow. It covers the idea that Agile practices such as Scrum, Kanban, and Extreme Programming (XP) help to gather data, model training, validation, model deployment and model iteration for AI projects. Additionally, the study also analyzes the impact that Agile has on mitigating the risks of the development, increase the quality of the model and increase the collaboration of the team in the AI centric software project. Through a synthesis of existing researches, empirical findings, the article provides with insights on good integration of Agile principles and AI development lifecycles.
subject: Articles
  topic: section
subject: Agile Methodologies
  topic: keywords
subject: AI Powered Development
  topic: keywords
subject: Scrum
  topic: keywords
subject: Kanban
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Iterative Development
  topic: keywords
creator: Sara Syed
rights: Copyright (c) 2025 Sara Syed https://creativecommons.org/licenses/by/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 10
ending page: 19

Attention Based Multimodal Sensor Fusion for Fall Detection Using Wearable Time-Series Data
type: item
description: Injuries and disabilities from falls are one of the leading causes of death, primarily in the world’s aging population, and is an even more severe problem within developing countries with fewer options for ongoing and consistent health care. The aim of study was to use a quantitative research design to analyze the effectiveness of attention-based multimodal sensor fusion techniques on fall detection involving wearable sensors within Pakistan. The study included a sample of 150 participants who were simulated performing both falls and other daily living activities while wearing wearable technologies with accelerometer and gyroscope sensors. Data collected were analyzed using existing attention-based multimodal fusion techniques on the wearable time series datasets, comparing the results against single sensor techniques, threshold-based techniques, and non-attention based deep learning techniques. Using only the accelerometer and gyroscope, each method achieved good accuracies, approximately 85.2% and 82.4% respectively. However, the results from the attention-based fusion approach for the various modalities showed significantly improved results for detecting falls with an accuracy of 92.8% (F1-score = 90.8% and false positive rate = 7.2%). The improvement in accuracy compared to the threshold-based approaches (p < 0.001) and non-attentional deep learning methods (p = 0.004) was statistically significant. Additionally, the feasibility analyses provided evidence that the system has strong real-time processing capabilities (M = 4.21) and feasibility for overall real-world deployment (M = 4.12). It is concluded that attention-based multimodal sensor fusion can increase both the accuracy and practicality of wearable fall detection systems making them suitable for implementation in low-resource health systems like that of Pakistan.
subject: Articles
  topic: section
subject: wearable devices
  topic: keywords
subject: fall detection
  topic: keywords
subject: multimodal sensor fusion
  topic: keywords
subject: attention mechanism
  topic: keywords
creator: Ayesha Ashraf
rights: Copyright (c) 2025 Ayesha Ashraf https://creativecommons.org/licenses/by/4.0
date: 2025-10-06
publication date: 2025-10-06
starting page: 01
ending page: 09

Robotics-Assisted Automation for Manufacturing Sector Efficiency
type: item
description: Robotics assisted automation has become one among the most important strategies in the manufacturing industries for bettering the overall operational efficiency as well as reducing the cost of production and aiding in improving the quality of products. This paper analyses the effect of robotics assisted systems on the manufacturing processes as the robot systems help in doing the repetitive processes and errors are minimized and the process is optimized. From the qualitative and quantitative data gathered from multiple manufacturing units, this research identifies important factors related to successful implementation: technological infrastructure, adaptation at the workforce level, and economic viability. Strategic adoption is proposed to not only improve productivity but also introduce sustainable practices in the sector. The insights provided will enable industry stakeholders to take advantage of the potential of automation to maintain their competitive edge as the environment in industry continues to evolve.
subject: Articles
  topic: section
subject: Robotics-aided automation
  topic: keywords
subject: Manufacturing efficiency
  topic: keywords
subject: Industrial automation
  topic: keywords
subject: Productivity optimization
  topic: keywords
subject: Workforce adjustment
  topic: keywords
subject: Manufacturing technology
  topic: keywords
subject: Process improvement
  topic: keywords
creator: Saba Syed
rights: Copyright (c) 2025 Saba Syed https://creativecommons.org/licenses/by/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 37
ending page: 42

Solar‑Powered Autonomous Drone System for Precision Agriculture
type: item
description: Against this a global transformation of agriculture is undertaken through precision-agriculture techniques that depend on timely and high-resolution monitoring of crops, soil and environmental parameters. One of the technologies that can deliver this has been in the form of unmanned aerial vehicles (UAVs) or drones, which are limited by limited battery capacity and often require re-charging thus limiting their use in large scale or off grid farms. The proposed research aims to evaluate and develop a solar-powered autonomous drone system (SPADS) to perform precision agriculture and morphological surveillance of crops, identifying stress and irrigation requirements (computer vision + ML) with advanced sensor packages (multispectral, thermal, RGB) and solar energy (photovoltaic power) as a low-carbon energy source (long autonomous mission) due to its solar capabilities. The paper outlines system design, data collection and experimental implementation in test plots, and provides the results that SPADS can work with full daylight cycles, generate high quality maps of vegetation and moisture and identify the early signs of crop stress with an 94 percent success rate. The system saves time, energy experiences and increases the scope of precision agriculture over distantly placed or resource constrained farms. The results justify the use of solar-powered UAVs as a potential and scalable solution to sustainable and energy-efficient monitoring and management of crops.
subject: Articles
  topic: section
subject: Long-term crop monitoring
  topic: keywords
subject: Stress detection
  topic: keywords
subject: Yield prediction
  topic: keywords
subject: Low energy cost
  topic: keywords
subject: precision-agriculture
  topic: keywords
creator: Furqan Naseer
rights: Copyright (c) 2025 Furqan Naseer https://creativecommons.org/licenses/by/4.0
date: 2025-08-26
publication date: 2025-08-26
starting page: 27
ending page: 36

Design and Simulation of Energy‑Efficient Electric Vehicle Charging Stations
type: item
description: Scalability, efficiency and sustainability Electric vehicle (EV) adoption requires scalable, efficient and sustainable charging infrastructure across the globe. Old grid-tied EV charging stations are capable of overloading the power system, particularly at peak hours. This study suggests a design structure and a simulation model of energy-efficient EV charging stations (EE-EVCS) based on renewable energy (solar PV), battery energy storage systems (BESS), and smart energy management approaches. With MATLAB/simulink, simulating different profiles of EV charging loads, a combination of PV generation, battery storage, and grid supply is made, aiming at the maximization of energy usage, decreasing grid reliance, and minimizing operation expenses. The findings indicate that hybrid charging stations have the potential to charge multiple EVs with reliability, draw less than 65 percent of the grid during peak periods, have a high charging station utilization efficiency (> 92%), and provide stable power quality. The results prove that renewable-augmented, storage-backed EV charging infrastructure is a feasible, environment-friendly solution to electrify the transport in the future.
subject: Articles
  topic: section
subject: Electric Vehicle Charging Station
  topic: keywords
subject: Renewable Energy
  topic: keywords
subject: Solar PV
  topic: keywords
subject: Battery Energy storage system (BESS)
  topic: keywords
subject: energy management
  topic: keywords
subject: simulation
  topic: keywords
subject: grid integration
  topic: keywords
subject: Power quality
  topic: keywords
subject: EV Infrastructure
  topic: keywords
creator: Sara Syed
rights: Copyright (c) 2025 Sara Syed https://creativecommons.org/licenses/by/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 20
ending page: 26

Deep Learning Algorithms for Predictive Maintenance in Industrial Machinery
type: item
description: Predictive maintenance (PdM) has become one of the most vital technological changes in the industrial machinery that allow organizations to leave the old scheduled maintenance approach and make decisions and actions using data and based on conditions. The improved ability of modeling nonlinear trends, managing high-dimensional sensor data, and learning complex time-series signals makes deep learning an important addition to the PdM in the current Industry 4.0 setting. The paper will give a comprehensive exploration of predictive maintenance technologies based on deep-learning, how they are applied, their comparative advantages, and their practical performance. The paper involves a thorough literature review, an intensive mechanism of creating PdM systems, a carefully-organized analysis of data and results, and a general discussion of the implications to industrial sectors. The study ends with a set of recommendations on how to enhance the adoption of PdM and the management of industrial assets using the deep learning techniques.
subject: Articles
  topic: section
subject: Predictive maintenance
  topic: keywords
subject: industrial machinery
  topic: keywords
subject: high-dimensional sensor data
  topic: keywords
subject: complex time-series signals
  topic: keywords
subject: deep-learning
  topic: keywords
creator: Ibtissam Essadik
rights: Copyright (c) 2025 Ibtissam Essadik https://creativecommons.org/licenses/by/4.0
date: 2025-07-26
publication date: 2025-07-26
starting page: 08
ending page: 19

AI‑Based Traffic Flow Prediction Models Using Real-Time Data
type: item
description: Vision Language Models (VLMs) have quickly come to dominate as a ground-breaking type of multimodal artificial intelligence systems with the ability to comprehend not only visual but also linguistic input. Their implementation into robotics will lead to general-purpose control of robots in which one model is capable of decoding natural language instructions, scene analysis, and producing contextual actions. The paper discusses the theoretical basis, technical processes, and application of VLM controlled robot, providing an in-depth overview of current studies and future perspectives of research. In a discussion of transformer architectures, multimodal encodings and robot behavior generation pipes, the paper identifies how VLMs can enable robots to reason like humans. Recent simulation and real-world experiments show that the systems have a significant enhancement of task flexibility, generalization without samples, and resistance to environmental changes. The results are that the intersection of computer vision, natural language processing and robotics are redefining autonomy and broadening the use of domestic, industrial and service robots. Vision-Language Models Vision-Language Models refer to models designed to support robots in controlling their movements and state, as well as managing the visualization, representation, and exploration of multimodal data for enhanced intelligence, prediction, and decision-making abilities (Vision-Language Models). Robot Control Multimodal AI Multimodal AI (Vision-Language Models) Multimodal data-visualization, -representation, and -exploration Multimodal data-visualization and -representation Multimodal data-visualization refers to a visual.
subject: Articles
  topic: section
subject: Traffic Flow Prediction
  topic: keywords
subject: Real-Time Data
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: LSTM
  topic: keywords
subject: CNN
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Intelligent Transportation Systems (ITS)
  topic: keywords
subject: Data Streaming
  topic: keywords
creator: Naqvi syed Ali Jafar
rights: Copyright (c) 2025 Naqvi syed Ali Jafar https://creativecommons.org/licenses/by/4.0
date: 2025-07-18
publication date: 2025-07-18
starting page: 01
ending page: 07

Socially Intelligent Robots for Healthcare and Assistive Applications
type: item
description: SIRs are socially intelligent, which combines artificial intelligence, natural language processing, computer vision, and affective computing and allows them to engage with humans in a natural manner in a healthcare and assistive setting. These robots will be programmed to offer companionship, daily living, cognitive, and therapeutic interventions. The article discusses the evolution, uses and assessment of SIRs in healthcare context with references to patient-centered design, safety, and adaptable learning. The experimental research reveals better engagement of patients, efficiency of tasks and emotional comfort. The results emphasize the significance of social intelligence to better the interaction between the robots and humans and provide helpful health and supportive services.
subject: Articles
  topic: section
subject: socially intelligent robots
  topic: keywords
subject: healthcare robotics
  topic: keywords
subject: assistive technology
  topic: keywords
subject: human-robot interaction
  topic: keywords
subject: affective computing
  topic: keywords
subject: adaptive learning
  topic: keywords
subject: patient care
  topic: keywords
creator: Muhammad Talal Aslam
rights: Copyright (c) 2025 Muhammad Talal Aslam https://creativecommons.org/licenses/by/4.0
date: 2025-06-21
publication date: 2025-06-21
starting page: 32
ending page: 39

Reinforcement Learning for Autonomous Navigation in Dynamic Environments
type: item
description: Reinforcement Learning (RL) has become a strong computational model that empowers robots and autonomous systems to acquire strong navigation behaviors via environment interaction. In dynamic environments where obstacles move, layouts change and the human activity is unpredictable, the traditional rule-based navigation solutions often do not work since they lack adaptability to the ever-changing environment. Deep reinforcement learning (DRL) and other RL methods provide the possibility to discover the best navigation policies that can be generalized to new unobservable situations. The article focuses on the application of RL in autonomous navigation in dynamic environments, including warehouses, outside streets, and multi-agent robots. It gives a systematic overview of the literature available, presents the methodology used, assesses benchmark analysis, and summarizes significant results. Safety, computational demand, real-time decision-making, and sim-to-real transfer are the other issues that are discussed in the paper. Future work recommendations and better system reliability are mentioned.
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Autonomous Navigation
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subject: Deep RL
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subject: Dynamic Environments
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subject: Robot Control
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subject: Policy Learning
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creator: Muhammad Amir
rights: Copyright (c) 2025 Muhammad Amir https://creativecommons.org/licenses/by/4.0
date: 2025-06-06
publication date: 2025-06-06
starting page: 25
ending page: 31

Safe and Explainable AI Techniques to Human-Robot Collaboration
type: item
description: Human-robot collaboration (HRC) has become a revolutionary paradigm of industrial, healthcare, and assistive robotics. To work well, robots are not only expected to work efficiently but also operate in a safe environment with human beings and ensure that their activities are transparently explained. Safe AI will provide the robots with collision prevention, compliance with physical limitations, and workspace consideration, whereas the Explainable AI (XAI) will improve the interpretability, confidence, and human control of the collaborative space. In this article, the design, implementation and evaluation of the HRC systems has been investigated based on the use of safe and explainable AI. We deconstruct models of intent modeling, risk-conscious control, and decision description, examine experimental data in the fields of manufacturing, healthcare, and assistive robotics, and interpolate the results of efficiency, safety, and human trust. The findings show that safety aware algorithms are much more effective when used in conjunction with interpretable models and lead to much fewer errors, greater efficiency in completing the tasks, and human confidence in autonomous systems.
subject: Articles
  topic: section
subject: Human-Robot Collaboration
  topic: keywords
subject: Safe AI
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subject: Explainable AI
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subject: Trustworthy Robotics
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subject: Adaptive Systems
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creator: Dur-E- Adan
rights: Copyright (c) 2025 Dur-E- Adan https://creativecommons.org/licenses/by/4.0
date: 2025-05-26
publication date: 2025-05-26
starting page: 17
ending page: 24

AI Driven Robotic Manipulation Using Multi Sensor Fusion
type: item
description: The real-world situations of robot manipulation usually require advanced perception and control measures to overcome the unpredictability, contacts, and occlusions. A strong solution, multi-sensor fusion, i.e. integration of modalities, which include vision, tactile/force, proprioception and in some cases audio, is integrated to complement information in making robust decisions. This paper discusses AI-based systems that combine several streams of sensory data to enable robots to perform intricate tasks through manipulation. We analyze hierarchical policy architectures, reinforcement learning (RL) and deep learning models which use multimodal inputs. By simulating and using real-robots, these systems exhibit increased manipulation precision, force control and generalization in different contact situations. The results demonstrate that the incorporation of vision and force/tactile feedback plays a vital role in enhancing the success of a task in assembly, grasping, and in-hand manipulation. We also examine issues such as synchronization, sensor noise as well as learning complexity and give recommendations to future studies. The data presented in this paper consist of a review of literature concerning robotic manipulation and sensor fusion, tactile sensing, vision, reinforcement learning and multimodal perception.
subject: Articles
  topic: section
subject: Robot Manipulation
  topic: keywords
subject: Advanced Perception
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subject: Multi-Sensor Fusion
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subject: Vision
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subject: Tactile/Force Sensing
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subject: Proprioception
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subject: Audio Sensing
  topic: keywords
subject: AI-Based Systems
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subject: Hierarchical Policy Architectures
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subject: Reinforcement Learning
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subject: Deep Learning
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subject: Multimodal Inputs
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subject: Simulation
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subject: Real-Robot Experiments
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subject: Manipulation Precision
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subject: Force Control
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subject: Generalization
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subject: Contact Situations
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subject: Task Success
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subject: Assembly
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subject: Grasping
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subject: In-Hand Manipulation
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subject: Sensor Synchronization
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subject: Sensor Noise
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subject: Learning Complexity
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creator: Daniyal Zaheer
rights: Copyright (c) 2025 Daniyal Zaheer https://creativecommons.org/licenses/by/4.0
date: 2025-05-07
publication date: 2025-05-07
starting page: 09
ending page: 16

Vision language Models of General Purpose Robot Control
type: item
description: ision Language Models (VLMs) have quickly come to dominate as a ground-breaking type of multimodal artificial intelligence systems with the ability to comprehend not only visual but also linguistic input. Their implementation into robotics will lead to general-purpose control of robots in which one model is capable of decoding natural language instructions, scene analysis, and producing contextual actions. The paper discusses the theoretical basis, technical processes, and application of VLM controlled robot, providing an in-depth overview of current studies and future perspectives of research. In a discussion of transformer architectures, multimodal encodings and robot behavior generation pipes, the paper identifies how VLMs can enable robots to reason like humans. Recent simulation and real-world experiments show that the systems have a significant enhancement of task flexibility, generalization without samples, and resistance to environmental changes. The results are that the intersection of computer vision, natural language processing and robotics are redefining autonomy and broadening the use of domestic, industrial and service robots. Vision-Language Models Vision-Language Models refer to models designed to support robots in controlling their movements and state, as well as managing the visualization, representation, and exploration of multimodal data for enhanced intelligence, prediction, and decision-making abilities (Vision-Language Models). Robot Control Multimodal AI Multimodal AI (Vision-Language Models) Multimodal data-visualization, -representation, and -exploration Multimodal data-visualization and -representation Multimodal data-visualization refers to a visual.
subject: Articles
  topic: section
subject: Vision-Language Models
  topic: keywords
subject: Multimodal Artificial Intelligence
  topic: keywords
subject: Robotics
  topic: keywords
subject: Robot Control
  topic: keywords
subject: Natural Language Instructions
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subject: Scene Analysis
  topic: keywords
subject: Transformer Architectures
  topic: keywords
subject: Multimodal Encodings
  topic: keywords
subject: Robot Behavior Generation
  topic: keywords
subject: Task Flexibility
  topic: keywords
subject: Generalization
  topic: keywords
subject: Environmental Robustness
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Multimodal Data Visualization
  topic: keywords
subject: Multimodal Data Representation
  topic: keywords
subject: Decision-Making Abilities
  topic: keywords
subject: Simulation Experiments
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subject: Real-World Experiments
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creator: Muhammad Asiel
rights: Copyright (c) 2025 Muhammad Asiel https://creativecommons.org/licenses/by/4.0
date: 2025-04-21
publication date: 2025-04-21
starting page: 01
ending page: 08

Use of Chatbots in Customer Service: A Technological Review
type: item
description: The merging of chatbots with the operations of the customer service has transformed the way the organizations interact with their customers through 24/7 support in an automated form, prompt response to queries, and affordable modes of communication. Chatbots, fueled by the progress of Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML), are becoming more and more indispensable in improving the customer experience and business efficiency. The paper presents a detailed technology analysis of chatbot solutions in customer service, its development, functionality, and performance in different sectors. It also looks at how conversational AI technologies can be used to create more human-like interfaces and mentions the drawbacks of linguistic ambiguity, issues of privacy with data, and dissatisfaction of the user with the inability to understand the context. The results indicate that the future of the chatbots is in the hybrid systems, which include the use of AI-based automation and human sensitivity to build the relationship and define the new standards of digital communication with the customers.
subject: Articles
  topic: section
subject: Chatbots
  topic: keywords
subject: Customer Service
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Natural Language Processing (NLP)
  topic: keywords
subject: Machine Learning
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subject: Conversational Agents
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subject: Automation
  topic: keywords
subject: User Experience
  topic: keywords
creator: Muhammad Amir
rights: Copyright (c) 2025 Muhammad Amir https://creativecommons.org/licenses/by/4.0
date: 2025-03-26
publication date: 2025-03-26
starting page: 35
ending page: 42

Role of Digital Innovation in Achieving Sustainable Development Goals (SDGs)
type: item
description: Digital innovation has become a disruptive element that has enhanced the realization of the United Nations Sustainable Development Goals (SDGs) faster. The Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data analytics are technologies that are transforming development strategies through improving efficiency, transparency, and inclusiveness in industries. Digital solutions help to achieve such goals as poverty reduction, education of high quality, access to healthcare, gender equality, and climate action. This paper will discuss how digital innovation can advance towards the SDGs, its multidimensional nature and provide possible challenges such as the digital divide, privacy, and the necessity to provide equitable access to technology. The paper stresses the importance of the responsible use of digital innovation in developing the sustainable and inclusive societies in accordance with the global 2030 Agenda by closing the developmental divide.
subject: Articles
  topic: section
subject: Sustainable Development Goals (SDGs)
  topic: keywords
subject: Digital Innovation
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Big Data
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subject: Block chain
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subject: ICT
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subject: Digital Transformation
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subject: Global Development
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subject: Innovation to Sustainability
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subject: Digital Inclusion
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creator: Muhammad Muzammil Asghar
rights: Copyright (c) 2025 Muhammad Muzammil Asghar https://creativecommons.org/licenses/by/4.0
date: 2025-03-15
publication date: 2025-03-15
starting page: 26
ending page: 34

Machine Learning Applications in Predicting Climate Change Patterns
type: item
description: The increasing influence of the climate change and its associated impacts has further given rise to the development of advanced computational tools to understand, predict and mitigate the environmental risks. As a subfield of Artificial Intelligence (AI), Machine Learning (ML) offers powerful methods for the analysis of complex climate data, pattern recognition and the prediction of changes in the environment. This paper is focused on the application of ML in forecasting climate change patterns with special emphasis on the application of ML in forecasting the patterns of change in temperature, extreme weather, rise in sea-level, and emission of greenhouse gases. Incorporating the use of supervised, unsupervised and reinforcement learning, researchers are in a position to be able to reveal the underlying trends, increase decision making predictability and provide better climate mitigation and adaptation strategies. More importantly, a combination of remote sensing data, climate models, and ML algorithms will help create dynamic forecasting systems, which can be functional at global, regional, and local levels. Other challenges discussed by the paper include the heterogeneity of data, the explainability of the model, and the limitations of computing which are also recommended as new areas of future research. It is found that ML is not only complementary to the conventional climate modeling, but it yields revolutionary information on proactive environmental planning and sustainable policy formulation.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Climate Change prediction
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subject: Artificial Intelligence
  topic: keywords
subject: Environmental Modeling
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subject: Extreme weather forecasting
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subject: Data analytics
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subject: remote sensing
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subject: predictive modeling
  topic: keywords
subject: sustainability
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subject: greenhouse gas emission.
  topic: keywords
creator: Muhammad Asiel
rights: Copyright (c) 2025 Muhammad Asiel https://creativecommons.org/licenses/by/4.0
date: 2025-02-27
publication date: 2025-02-27
starting page: 18
ending page: 25

Green Computing: Reducing Carbon Footprints Through Energy-Efficient Technologies
type: item
description: Rapid development of information technology and computing systems has led to significant increase in the power requirements and environmental footprint which led to an increase in the concern towards carbon emissions and sustainability. One such approach that is helping us in checking the degradation of the environment is a method known as green computing which is concerned with energy-efficient hardware, sustainable hardware design, and environmentally conscious operation. This paper will discuss the principles, strategies, and technological advancements in green computing and how they have contributed to reducing carbon footprints in different computing environments. It also considers the energy-efficient hardware, virtualization, cloud computing and smart resource management as important sustainable IT operational mechanisms. Other problems that will be addressed in the study include cost, technology adoption barrier, and performance vs. sustainability trade off. Results show that green computing can have a major influence on reducing the energy consumption and carbon emissions, which will supply eco-friendly and economically viable computing systems. The paper concludes with a conclusion and suggestions for organizations and policymakers on the necessity to promote IT infrastructure and practices to make them sustainable.
subject: Articles
  topic: section
subject: Green Computing
  topic: keywords
subject: Energy efficiency
  topic: keywords
subject: Carbon footprint
  topic: keywords
subject: Sustainable IT
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Virtualization
  topic: keywords
subject: Resource optimization
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subject: Eco friendly technologies
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creator: Furqan Naseer
rights: Copyright (c) 2025 Furqan Naseer https://creativecommons.org/licenses/by/4.0
date: 2025-02-12
publication date: 2025-02-12
starting page: 9
ending page: 17

Biometric Authentication Systems: Privacy Challenges and Technological Advances
type: item
description: The biometric authentication system is used to verify the identity of the user by using the unique physiological and behavioral characteristics like fingerprints, iris scan, facial characteristics, and voice, which is one of the main reasons for its fast growth in the personal, corporate, and government sectors. The present paper discusses the two sides of the biometric authentication problem: technologies that have enhanced the recognition accuracy, the spoof-resistance and the comfort of the users, and the privacy issues that may arise from the collection, storage and irreversibility of the biometric data. Emerging modalities (e.g., behavioral biometrics, brain-wave authentication) and multi-modal fusion, cancellable templates and privacy ensuring methods, e.g., federated learning and homomorphic encryption, are discussed. At the same time, the paper explores some of the most important privacy issues, such as irreparability of breached characteristics, mass surveillance, data breach, and loopholes. The results show that although biometric systems are more or less safe and convenient, to ensure the privacy, a protection strategy, including various types of technological protection, user agreement, and policy enforcement actions is required.
subject: Articles
  topic: section
subject: Behavioral biometrics
  topic: keywords
subject: Cancellable templates
  topic: keywords
subject: Homomorphic encryption
  topic: keywords
subject: Multi-modal biometrics
  topic: keywords
subject: Privacy issues
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subject: Regulatory frameworks
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subject: Spoof resistance
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creator: Dur-E- Adan
rights: Copyright (c) 2025 Dur-E- Adan https://creativecommons.org/licenses/by/4.0
date: 2025-01-17
publication date: 2025-01-17
starting page: 1
ending page: 8