ComputeX - Journal of Emerging Technology & Applied Science
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.
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Role of GIS Technology, Spatial Data Analysis, and Remote Sensing in Urban Planning
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.
Copyright (c) 2026 Muhammad Saleem Ashraf, Hamza Khan https://creativecommons.org/licenses/by/4.0
Mathematical Modeling of Epidemic Disease Spread Considering Population Density, Vaccination Rate, and Human Mobility Patterns
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.
Copyright (c) 2026 Muhammad Adeel Hassan, Faizan Akram https://creativecommons.org/licenses/by/4.0
Design of Energy-Efficient Wireless Communication Networks Using IoT-Based Traffic Management
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.
Copyright (c) 2026 Ayesha Perveen, Zubair Shakeel Khan https://creativecommons.org/licenses/by/4.0
Data Science Applications in Environmental Monitoring Using Remote Sensing Data, Machine Learning Algorithms, and IoT-Based Sensors
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.
Copyright (c) 2026 Nida Qureshi, Muhammad Imran Siddiqui, Shiza Malik https://creativecommons.org/licenses/by/4.0
Role of Cloud Computing, Data Storage Scalability, and Distributed Computing Frameworks in Improving Big Data Processing Efficiency
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.
Copyright (c) 2026 Hina Ashraf, Ali Raza Shah, Farooq Asghar https://creativecommons.org/licenses/by/4.0
Intelligent Workflow Management Systems in Educational Institutions
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.
Copyright (c) 2025 Saba Syed https://creativecommons.org/licenses/by/4.0
Digital Twin Models for Simulation and Optimization of Processes
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.
Copyright (c) 2025 Daniyal Zaheer https://creativecommons.org/licenses/by/4.0
AI-Assisted Software Testing and Bug Prediction
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.
Copyright (c) 2025 Dur-E- Adan https://creativecommons.org/licenses/by/4.0
Agile Methodologies for AI-Driven Application Development
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.
Copyright (c) 2025 Sara Syed https://creativecommons.org/licenses/by/4.0
Attention Based Multimodal Sensor Fusion for Fall Detection Using Wearable Time-Series Data
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.
Copyright (c) 2025 Ayesha Ashraf https://creativecommons.org/licenses/by/4.0
Robotics-Assisted Automation for Manufacturing Sector Efficiency
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.
Solar‑Powered Autonomous Drone System for Precision Agriculture
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.
Copyright (c) 2025 Furqan Naseer https://creativecommons.org/licenses/by/4.0
Design and Simulation of Energy‑Efficient Electric Vehicle Charging Stations
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.
Deep Learning Algorithms for Predictive Maintenance in Industrial Machinery
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.
Copyright (c) 2025 Ibtissam Essadik https://creativecommons.org/licenses/by/4.0
AI‑Based Traffic Flow Prediction Models Using Real-Time Data
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.
Intelligent Transportation Systems (ITS)
Copyright (c) 2025 Naqvi syed Ali Jafar https://creativecommons.org/licenses/by/4.0
Socially Intelligent Robots for Healthcare and Assistive Applications
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.
Copyright (c) 2025 Muhammad Talal Aslam https://creativecommons.org/licenses/by/4.0
Reinforcement Learning for Autonomous Navigation in Dynamic Environments
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.
Copyright (c) 2025 Muhammad Amir https://creativecommons.org/licenses/by/4.0
Safe and Explainable AI Techniques to Human-Robot Collaboration
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.
AI Driven Robotic Manipulation Using Multi Sensor Fusion
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.
Vision language Models of General Purpose Robot Control
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.
Copyright (c) 2025 Muhammad Asiel https://creativecommons.org/licenses/by/4.0
Use of Chatbots in Customer Service: A Technological Review
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.
Role of Digital Innovation in Achieving Sustainable Development Goals (SDGs)
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.
Copyright (c) 2025 Muhammad Muzammil Asghar https://creativecommons.org/licenses/by/4.0
Machine Learning Applications in Predicting Climate Change Patterns
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.
Green Computing: Reducing Carbon Footprints Through Energy-Efficient Technologies
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.
Biometric Authentication Systems: Privacy Challenges and Technological Advances
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.