Journal of Artificial Intelligence and Computing
Journal of Artificial Intelligence and Computing (JAIC) is a peer-reviewed, scientific and technical journal owned and published by Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan. JAIC publishes high-quality original scientific articles dealing with the use of analytic and quantitative tools for the modeling, analysis, design and engineering management in the Engineering and Technology disciplines. Scope and Aim of the Journal JAIC provides a platform for the researchers, academicians, professionals, practitioners, and students to impart and share knowledge in the form of high-quality empirical and theoretical research papers, case studies, review papers, analytical and simulation models, technical notes from researchers, academicians, professionals, practitioners and students from all over the world. The journal's scope falls in all fields of Electrical engineering, Electronics and communication engineering, Information Security, Machine Learning, Artificial Intelligence, Software and Computer Engineering and Biomedical Engineering. The journal does not charge any Article Processing Charges (APCs)/fees for the publication and submission of the articles. The JAIC Journal makes a maximum effort to publish submitted papers as quickly as possible. The first round of reviews should be completed within two months. Then, authors are expected to improve their manuscript within one month. The publication time is prolonged if substantial changes are recommended and the reviewers request further reviews. The whole review process fully follows standard procedures. E-ISSN = 3005-3358 Publication Frequency = Bi-Annually (June, December) The journal does not charge any Article Processing Charges (APCs)/fee for publication and/or submission of the articles.
Swedish College of Engineering and Technology
Governing Artificial Intelligence by Standard and by Statute: A Comparative Review of ISO/IEC 42001 and the EU Artificial Intelligence Act
Artificial Intelligence is increasingly deployed across healthcare, finance, public administration, and education, creating value while introducing technical, ethical, and legal risks that require effective governance. ISO/IEC 42001:2023, the first certifiable AI management system standard, and the EU Artificial Intelligence Act, the first comprehensive binding AI regulation, have emerged as the two most influential governance instruments. This review compares their origins, scope, architecture, and governance approaches, examining their concerns, overlap, complementarity, strengths, and limitations. Findings show that the two instruments are complementary rather than competing: ISO/IEC 42001 offers a voluntary, process-based, organization-wide framework for responsible AI governance, while the EU AI Act establishes a product-centric, risk-based legal framework backed by enforcement, together forming a two-tier governance structure. The review recommends an integrated approach in which ISO/IEC 42001, supported by the NIST AI Risk Management Framework, helps operationalize compliance with the Act, and calls for dynamic risk assessment, sector-specific adaptation, and greater international harmonization.
Copyright (c) 2026 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
Industrial IoT–Edge Computing Security: A Comprehensive Review of Threats, Mitigation Strategies, and Future Directions
Recent advancements in Industrial Internet of Things (IIoT) systems introduce two major trends: connecting with edge computing and the connectivity with Cyber-Physical Systems (CPS) that bring in real-time industrial automation, while exposing a much broader threat surface than conventional IT networks. This paper reviews a systematic survey that presents a comprehensive analysis of IIoT architectures, edge computing integration, CPS vulnerabilities, and a complete attack taxonomy spanning the perception, network, processing, and application layers. The review evaluates AI/ML-based intrusion detection systems, federated learning frameworks, blockchain-based audit mechanisms, cryptographic protocols, and Zero Trust Architecture as security countermeasures. Key findings confirm that advanced combinations of machine learning, federated learning, blockchain, and IDS are essential for data protection and real-time attack detection. Critical gaps are identified in standardized benchmarking environments, hardware-level security, and quantum-safe cryptographic migration pathways.
Security in IoT Devices against DDoS Attacks: Strategies and Mitigation Techniques
The rapid growth of Internet of Things (IoT) deployments has increased the threat of Distributed Denial of Service (DDoS) attacks that use resource-constrained devices to disrupt critical services. This paper conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines and synthesizes 62 studies published between 2023-2026 retrieved from IEEE Xplore, ACM, ScienceDirect, SpringerLink, Wiley, MDPI, and Scopus. The review classifies DDoS attack vectors, analyzes exploited IoT vulnerabilities, and evaluates mitigation strategies such as machine learning, blockchain, software-defined networking (SDN), and edge computing. Hybrid CNN-LSTM models can achieve up to 99.1% detection accuracies and federated learning is the fastest growing sub-area. It identifies key research gaps and a future roadmap prioritized.
IoT Forensics and Current Landscape and Future Trajectories: A Survey
Internet of Things (IoT) forensics focuses on detecting security incidents, collecting evidence, and analyzing digital traces in IoT environments. Unlike traditional digital forensics, it faces challenges due to limited device processing power, storage, and connectivity. This paper reviews the current state of IoT forensics through a qualitative analysis of existing literature. It identifies key challenges such as evidence acquisition, data preprocessing, anti-forensic techniques, and cloud-based data recovery. The study also discusses forensic readiness, documentation, privacy, and evidence presentation. The findings highlight the need for reliable forensic mechanisms and provide directions for future research in IoT forensic investigations.
Digitization of Enterprises: Assessing the Adoption and Impact of Digital Tools in Small and Medium Enterprises in Gilgit-Baltistan
This study investigates the adoption and impact of digital tools among Small and Medium Enterprises (SMEs) in the remote region of Gilgit-Baltistan, Pakistan, with a specific focus on gender differentials. Employing a mixed-methods research design, the study analyzes data from 55 SMEs through quantitative surveys and in-depth qualitative interviews. The findings reveal a "mobile-first" digital landscape where social media and communication apps (e.g., WhatsApp, Instagram) are the dominant business tools, while adoption of advanced systems like ERP remains low. Statistical analysis demonstrates a strong, positive relationship between digital adoption and business performance, with regression results indicating that digital maturity is a significant predictor of revenue growth (R2 = 0.658). However, the qualitative analysis identifies severe infrastructural deficits—specifically unreliable internet connectivity—as the primary barrier to scalability. Furthermore, the study highlights a distinct gender divide: while female entrepreneurs face greater structural constraints regarding training and finance, they are successfully leveraging social commerce to bypass traditional mobility restrictions and access wider markets. The research concludes that while digitalization is a potent driver of economic resilience in Gilgit-Baltistan, unlocking its full potential requires targeted policy interventions in physical infrastructure and gender-inclusive digital literacy programs.
Leveraging Transformer-Based Natural Language Processing for Adaptive Language Learning in Digital Humanities Environments
The convergence of transformer-based natural language processing (NLP) and digital humanities (DH) has opened unprecedented opportunities for automated language learning, discourse analysis, and pedagogical innovation. This study presents a corpus-driven computational investigation into the efficacy of large language models (LLMs) and domain-specific fine-tuned transformers; particularly BERT, RoBERTa, and T5; in supporting adaptive language learning platforms, automated assessment, and semantic retrieval within digital humanities contexts. Drawing on a multi-source corpus of 102,620 documents spanning academic journals, literary archives, e-learning transcripts, social media data, parliamentary debates, and news corpora (totalling over 13.1 million tokens), the research employs a mixed-methods design integrating quantitative NLP evaluation metrics (accuracy, Cohen's kappa, BLEU, ROUGE) with qualitative thematic analysis. Findings reveal that fine-tuned transformer models achieve accuracy rates between 79.4% and 94.3% across core language tasks, with adaptive e-learning applications demonstrating a 78% improvement in learner retention. Five overarching themes emerge: algorithmic bias, multimodal integration, low-resource language processing, human-AI collaborative writing, and real-time assessment. The study contributes a validated six-phase methodological framework for deploying NLP in DH-driven educational environments and advances theoretical understanding of computational pedagogy at the intersection of language, technology, and culture.
Generative AI as an Automated Written Corrective Feedback Provider in EFL Academic Writing: A Mixed-Methods Investigation of Accuracy Gains, Feedback Quality, and Learner Engagement
The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into language technology has reopened long-standing questions about the role of automated written corrective feedback (AWCF) in second-language (L2) writing development. While early enthusiasm has positioned conversational LLMs such as GPT-4 as scalable substitutes for the labour-intensive provision of teacher feedback, the empirical base remains fragmented, and few controlled studies examine accuracy outcomes, feedback quality, and learner engagement together. This study addresses that gap through a controlled investigation conducted in an under-represented South Asian English-as-a-foreign-language (EFL) setting. Adopting a sequential explanatory mixed-methods design, ninety undergraduate EFL learners were stratified and randomly assigned to three conditions—GPT-4 AWCF, expert teacher-written corrective feedback, and a self-revision control—across an eight-week, six-cycle argumentative writing programme. Quantitative strands comprised error-rate analysis, an analytic rubric, and a validated engagement questionnaire; qualitative strands comprised corpus coding of 1,860 feedback moves, stimulated-recall interviews, and reflective journals, integrated through a joint display. Analysis of covariance indicated that the GPT-4 group significantly outperformed the control on both accuracy and rubric scores, with gains statistically comparable to, and on local accuracy marginally exceeding, the teacher group, and with most gains retained at delayed post-testing. Feedback coding revealed that GPT-4 favoured metalinguistic explanation and reformulation but showed reduced precision on discourse-level concerns, where redundancy and occasional hallucinated corrections appeared. Engagement was high—particularly on the affective dimension—but cognitively demanding, and learners expressed concern about over-reliance and loss of authorial voice. The study contributes controlled, integrated evidence on the pedagogical value and limitations of LLM-based language technology and argues for a principled human-in-the-loop model of AI-assisted feedback rather than wholesale automation.
natural language processing in education
Cybersecurity Challenges, Vulnerabilities, and Emerging Security Solutions in the Internet of Things
IoT has been revolutionising the world of digital ecosystems by enabling smart, interconnected devices to exchange information across domains such as healthcare, transportation, industry, smart cities, and many others. As IoT expands, emerging cybersecurity challenges stem from resource constraints, heterogeneity, weak authentication, software vulnerabilities, and insecure channels. The proposed survey discusses the prominent security challenges and vulnerabilities in IoT frameworks and presents potential solutions like lightweight cryptography, blockchain, AI-powered IDS, federated learning, and secure authentication schemes. The research highlights the importance of a layered and flexible security approach to enhance IoT security posture, covering its confidentiality, integrity, and availability, so that we can build a secure and robust IoT world.
The Overwhelming Applications of Artificial Intelligence in Every Field of Life: A Comprehensive Review
Artificial Intelligence (AI) has emerged as one of the most revolutionary technological advancements in human history, significantly transforming the way individuals, industries, and societies operate. By enabling machines to imitate cognitive functions such as learning, reasoning, perception, problem-solving, and decision-making, AI has expanded beyond the boundaries of traditional computing and become a fundamental driver of modern digital transformation. The rapid development of computational power, the availability of massive datasets, and the advancement of sophisticated algorithms have accelerated the integration of AI technologies into nearly every field of human life. Modern AI systems are built upon multiple interdisciplinary technologies, including machine learning, deep learning, artificial neural networks, natural language processing, computer vision, robotics, and intelligent automation. These technologies enable machines to analyse complex information, recognise patterns, understand human language, process visual data, and perform tasks with efficiency that often surpasses traditional methods. Consequently, AI has become an indispensable component of healthcare, education, engineering, manufacturing, agriculture, transportation, finance, cybersecurity, environmental monitoring, and daily consumer applications. In healthcare, AI-based diagnostic systems assist physicians in detecting diseases from medical images and predicting patient outcomes. In education, intelligent tutoring systems provide personalised learning experiences according to individual student requirements. Industrial sectors utilise AI-driven automation, predictive maintenance, and robotic systems to enhance productivity and reduce operational costs. Similarly, AI-powered applications in transportation, smart cities, agriculture, and energy management contribute toward building sustainable and efficient infrastructures. Despite its remarkable advantages, the widespread adoption of AI also introduces several challenges related to data privacy, algorithmic bias, transparency, cybersecurity, and the social consequences of automation. Addressing these concerns through ethical frameworks and responsible AI governance is essential to ensure that AI technologies remain beneficial for humanity. This paper presents a comprehensive review of the fundamental concepts, technological advancements, extensive applications, benefits, challenges, and future directions of artificial intelligence. The study demonstrates that AI is no longer limited to a specialized scientific discipline but has become a universal technology capable of transforming virtually every aspect of human existence.
Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
Towards Resilient Smart Cities: Security, Trust, and Sustainability in Edge Computing - A Survey
Edge computing has underpinned smart cities by enabling low-latency, localised intelligence for traffic, surveillance, and infrastructure. However, its decentralised, heterogeneous footprint poses severe risks to security, privacy, and environmental sustainability. This paper reviews secure, sustainable edge computing and introduces a unified framework balancing security with ecological constraints. It establishes a threat taxonomy covering expanded attack surfaces, identity flaws, and physical or AI-driven threats, then critically evaluates five core paradigms, AI anomaly detection, blockchain, secure virtualisation, and privacy analytics against latency and resource overhead. Crucially, we assess the environmental toll of these defenses using Energy per Operation (EPO), Energy-Delay Product (EDP), Carbon Intensity (CI), and Lifecycle Energy Use (LEU) metrics. Exposing a critical gap in the literature: the lack of standardised benchmarks for security carbon costs. Finally, we map future frontiers like post-quantum crypto, secure federated learning, digital twins, and edge-native zero-trust, proving that modern urban ecosystems must treat security and sustainability as co-dependent design requirements. The findings emphasise that future smart-city infrastructures must balance security, privacy, performance, and sustainability to achieve resilient and trustworthy edge computing ecosystems.
Future of Artificial Intelligence with the Perspective of Deep Learning: A Review
Artificial Intelligence (AI) has become one of the most transformative technologies of the modern era, influencing diverse domains including healthcare, transportation, manufacturing, education, finance, and communication. The rapid evolution of AI has been largely driven by advances in deep learning, a subset of machine learning inspired by the structure and function of the human brain. Deep learning has enabled machines to process complex data, recognize patterns, understand natural language, and make intelligent decisions with unprecedented accuracy. This review paper explores the future trajectory of AI through the perspective of deep learning, discussing its current achievements, emerging trends, opportunities, and challenges. The paper highlights the development of advanced neural architectures, generative AI, multimodal learning, explainable AI, edge intelligence, and autonomous systems. Furthermore, ethical concerns, computational limitations, and the necessity for trustworthy AI are examined. The review concludes that deep learning will remain a fundamental pillar of future AI systems, while its integration with other technologies and human-centered principles will determine the direction of intelligent systems.
Analysis of an Anti-Aliasing and Reconstruction System
This research develops a complete signal-processing chain for a digital electrocardiogram (ECG) monitor. The signal contains 20 Hz and 60 Hz cardiac components plus 150 Hz noise. A 6th-order Butterworth low‑pass filter with an 80 Hz cut-off removes the noise before sampling at 250 Hz, preventing aliasing. The system uses causal zero-order hold reconstruction and is proven stable, linear, and causal. MATLAB simulations validate accurate signal recovery with minimal distortion.
Smart Grid Innovation: Machine Learning for Real-Time Energy Management and Load Balancing
When machines learn to predict patterns, power networks start making smarter decisions on their own. Instead of just moving electricity one-way, modern systems talk back and forth, adjusting flow based on what's happening right now. These updates help handle sudden spikes in usage, though spreading supply evenly remains tricky. Because conditions shift constantly, old methods struggle to keep up - here is where pattern-based computing steps in. Using historical records plus live inputs, these tools spot trends before they peak. One approach digs through sequences of past use; another learns best moves by testing outcomes over time. Predicting tomorrow’s draw becomes less guesswork when software adapts from experience. Adjustments happen faster when rules aren’t fixed but are shaped by new information. Stability grows not from brute strength but from subtle corrections made minute after minute. What used to take teams of engineers now runs quietly behind the scenes. Implementing these tools in today’s smart grid systems means addressing issues around data collection, processing, and handling heavy computational loads. Real examples show that machine learning works well when used in actual grid operations, boosting both performance and dependability. Looking ahead, progress in learning models stands out, along with folding solar and wind power into the mix, while keeping user data safe matters too. Power networks could change completely as machines learn to manage electricity better, reacting faster and adjusting on their own. What researchers are uncovering helps make sense of using new tech to address ongoing and emerging problems in how we handle energy.
Design and Implementation of IoT-Based Smart Meter
Pushed through the call for sustainable electricity control, the clever metering era has advanced to offer real-time usage monitoring, remote reading, and bidirectional communication. However, the prevalence of single-phase meters limits software in business and commercial settings, that require greater capacity. Three-section clever meters are essential for providing accurate data in those sectors. This project proposes designing and building an IoT-enabled, 3-segment clever meter capable of measuring and transmitting time-intake statistics. The device will independently monitor every phase, utilising IoT to share data between software groups and consumers. This venture aims to illustrate the advantages of 3-section smart metering, specifically improved data accuracy, optimised energy consumption, and decreased operational costs.
Future of Programmable Logic Controller, Recent Trends, and Challenges
Programmable Logic Controller (PLC)- based systems play a fundamental role in industrial automation, modern automated systems, and industrial control systems, monitoring, controlling, and optimising complex processes across modern industries. PLCs are digital computers specifically designed to operate in harsh industrial environments such as manufacturing plants, power stations, oil refineries, and transportation systems. Unlike conventional control systems, which rely on electromechanical relays and wiring, PLCs use programmable software and modular hardware, providing flexible, efficient, and reliable automation solutions. The use of PLC technology has significantly improved productivity, reliability, and safety in industrial operations.
Systematic Study of Anti-Jamming Strategies Using Direct Sequence Spread Spectrum
Interference has always been a problem for wireless communication, but deliberate jamming is a distinct kind of issue. In contrast to random noise, a jammer is something that intentionally works against you. In fact, if the system is not designed to manage it, even a low-power jammer can entirely shut down a link. This is precisely the reason Direct Sequence Spread Spectrum (DSSS) was created. The basic concept is straightforward, spread the signal over a much larger bandwidth to eliminate any narrowband interference and prevent serious harm. This study examines how DSSS accomplishes the strategies used to control bit errors even in difficult channel conditions, as well as the design choices that go into building a strong anti-jamming system around it. Different types of spread spectrum are studied, as well as processing gain, error correction coding, interleaving, and adaptive techniques that change the system's behavior based on what the channel is doing at that specific moment.
Why SCADA: Fundamentals, Challenges and Future
Supervisory Control and Data Acquisition (SCADA) systems represent critical technology in modern industrial automation and control. This comprehensive research report examines the fundamental principles, architectural components, and operational mechanisms of SCADA systems. The report provides an in-depth analysis of current challenges facing SCADA implementations, particularly cybersecurity threats and integration complexities. Furthermore, it explores emerging trends and future directions, including the integration of Industrial Internet of Things (IIoT), cloud computing, artificial intelligence, and edge computing technologies. The findings indicate that while SCADA systems continue to evolve, addressing security vulnerabilities and embracing digital transformation remain paramount for industrial organizations.
Optimization of Wire Electrical Discharge Machining Parameters for Inconel Alloys: A Comprehensive Review of Hybrid Techniques
Superalloys, particularly nickel-based Inconel alloys, are extensively used in high-performance industries due to their superior strength, corrosion resistance, and thermal stability at elevated temperatures. However, their excellent mechanical properties also make them difficult to machine using conventional methods. Wire Electrical Discharge Machining (WEDM) has emerged as an effective non-traditional technique for precision machining of Inconel alloys, offering high accuracy and minimal residual stress. Numerous studies have employed optimization techniques such as Taguchi, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), Genetic Algorithms (GA), and hybrid approaches to enhance machining efficiency. Among various parameters, pulse-on time and pulse-off time have been identified as the most significant factors influencing Material Removal Rate (MRR) and Surface Roughness (SR). Although several researchers have optimized these parameters individually, comprehensive multi-objective optimization addressing both MRR and SR simultaneously remains limited. The literature review and analysis highlight a significant research gap in integrating statistical and intelligent optimization methods for improved machinability of Inconel alloys. This study aims to explore hybrid optimization techniques to achieve superior machining performance and surface integrity, thereby enhancing the reliability and applicability of superalloy components in aerospace, nuclear, and energy sectors.
Wire Electrical Discharge Machining (WEDM);
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Deep Learning for Predictive Maintenance in Smart Manufacturing — A Review
Deep learning (DL) has emerged as a transformative tool for predictive maintenance (PdM) and fault diagnosis across industrial domains such as aerospace, automotive, energy, and process systems. This review synthesizes 35 recent studies employing diverse DL models—including CNN, LSTM, Transformer, Autoencoder, GAN, GNN, and hybrid physics-informed architectures—applied to sensor, acoustic, vibration, and process signals. The findings reveal that DL significantly improves Remaining Useful Life (RUL) estimation, anomaly detection, and fault classification, outperforming traditional machine learning approaches. Despite these advances, challenges persist: large data requirements, limited cross-domain generalization, model interpretability gaps, high computational cost, unstable training in generative methods, and unclear thresholds in unsupervised detection. Moreover, most research is constrained to component-level validation, with limited industrial deployment. This review identifies critical research gaps and provides future directions to guide the development of scalable, explainable, and resource-efficient DL solutions for real-world predictive maintenance.
Nanofluids in Metal Cutting and Grinding: A Comprehensive Review
Nanofluids suspensions of nanoparticles in conventional cutting fluids—have emerged as promising alternatives to improve cooling, lubrication, and tribological performance in machining and grinding operations. This review synthesizes two decades of research (2003–2025) on nanofluid applications in metal cutting, with emphasis on their effects on tool wear, surface finish, cutting forces, and sustainability. Oxide-based nanofluids such as Al₂O₃ and ZnO consistently enhance thermal stability and cost-effectiveness, while carbon-based systems (CNTs, graphene) deliver superior tribological performance but face challenges of dispersion and high cost. Hybrid and eco-friendly formulations demonstrate synergistic benefits and support sustainable manufacturing, though scalability and long-term stability remain barriers. Advanced approaches, including cryogenic, field-assisted, and AI-integrated nanofluids, reveal breakthrough potential but are largely confined to laboratory studies. Reported improvements include up to 45% reduction in tool wear, 20–40% decrease in cutting forces, and 15–50% enhancement in surface finish. Persistent gaps include lack of standardized benchmarking, limited techno-economic validation, and health and safety concerns. This review concludes that industrial adoption of nanofluids will require development of scalable eco-friendly systems, comprehensive lifecycle assessments, and internationally accepted testing protocols.
Review on Surface Modification and Coating Effects in Wire EDM Machining Performance
Wire Electrical Discharge Machining (WEDM) is a widely adopted process for producing intricate and high-precision components where conventional machining methods face limitations. The performance of WEDM is strongly influenced by the properties of the wire electrode, with coated and surface-modified wires offering significant advantages over plain brass wires. This review systematically examines advancements in wire surface modification, from conventional zinc coatings to diffusion-annealed, nano-engineered, and composite structures. Reported improvements include higher material removal rates, reduced surface roughness, superior wear resistance, and greater dimensional stability. Despite these advances, critical gaps remain, particularly in the absence of standardized benchmarking protocols, comprehensive life-cycle and techno-economic evaluations, and scalable fabrication methods. Furthermore, sustainability aspects such as recycling, environmental footprint, and large-scale industrial validation are underexplored. Addressing these limitations will be essential to translate laboratory-level innovations into robust industrial solutions and to ensure the broader adoption of coated wire technologies in advanced manufacturing sectors.
Design and Implementation of a Home Appliance Automation Framework
The paper contains two sections. The first section explains the literature review about the existing Home automation systems with reference to the technologies used in each of them. The second section describes the proposed system’s design and implementation. Traditionally, the proposed system was to automate the home electrical devices and appliances for providing ease of use to the home owners within a limited scope, but later on, the advancements in the system led to a more scalable, flexible and remotely accessible approach towards control and management of home appliances. Nowadays, home automation is becoming more of a concern of cost-effectiveness and ubiquitous access. Numerous cost-effective technologies have been proposed, which are increasingly being used, but the concern of ubiquitous access is still a matter to be solved. There are cost-effective systems based on Wi-Fi, Bluetooth, etc. technologies, but their scope is limited as the home appliances can be controlled by the user only within the range of Bluetooth or from where the Wi-Fi network is available. These limitations are paving the way to the development of systems that will support cost-effective ubiquitous access to home electrical devices and appliances in the future. The proposed system will be cost-effective as it uses the mobile phone’s inbuilt SMS facility for the home appliances’ automation. The proposed system monitors the status of home appliances and controls them within the GSM network.
Harnessing Industrial Revolution Using Artificial Intelligence for Robust Economic Corridors and Sustainable Development
The advent of the Fourth Industrial Revolution (IR 4.0) was poised to reshape the global economy, underscored by UNESCO’s emphasis on digital transformation and innovation. Within this context, we investigate the integration of Autonomous Truck Platooning (ATP) within the China-Pakistan Economic Corridor (CPEC) and the Belt and Road Initiative (BRI). According to the UN, it affects over 60% of the world's population and spans over 65 countries. ATP, through the utilization of advanced sensors, communication technologies, and artificial intelligence, has the potential to significantly improve the efficiency, sustainability, and security of freight transportation. Employing a mixed-methods approach, this research engages with empirical data, including World Bank statistics on trade and transport, and conducts system modelling and stakeholder consultations. A noteworthy revelation is that, with an estimated 20% reduction in fuel costs and similar reductions in carbon emissions, ATP could contribute substantially to economic growth and environmental sustainability within the CPEC and BRI corridors. However, integrating this technology requires extensive infrastructural upgrades, regulatory harmonization, and social adaptation. Considering these considerations, this study culminates in quantifiable and discrete conclusions. A tangible outcome is the prospect of generating an estimated annual trade efficiency gain of US $ 5 billion across the corridors by 2030. Furthermore, the study delineates practical implications, policy recommendations, and propositions for future research, serving as a cornerstone for policymakers, stakeholders, and academia in understanding and capitalizing on IR 4.0 technologies for robust economic corridors and sustainable development.
Blood Cell Disease Prediction with Machine Learning
Our body has cells that provide oxygen to our cell nucleus, called red blood cells (RBC). The other cells that are fighting the disease in our body are called the White Blood Cell (WBC), and some types of cells prevent bleeding with clotting processes. There are three main types of blood cells. If we have some disorder in these cells, then our body faces diseases like leukemia, lymphoma and myeloma. Blood cell diseases can be identified using blood cell image symptoms, causes, and present conditions. Blood smear images with surrounding images of red, platelet, and white cell breakdown play an important role in assessing and diagnosing a large range of illnesses, including infection, disease, and leukemia. Due to the segmentation or breakdown procedures, some image processing techniques are performed to improve picture detection quality. Blood cell segmentation remains a serious challenge. That’s why deep learning technology is used to get the blood cell images clearly with the application of cutting-edge technology for the white blood cells, platelets and red blood cells in blood smear images. The accuracy of blood cell smear images required for automatic illness identification has been demonstrated in previous research experiments. To tackle this blood cell disease issue and optimize the system's performance to detect the diseases present in blood cells, we can find through their images by proposing deep learning.
Design and Experimentation of Peltier-Based Thermoelectric Cooling Vaccine Carrier Box
Vaccines are typically administered during routine immunization programs, with a critical focus on maintaining a cold chain system. The cold chain system plays a pivotal role in ensuring the efficacy of vaccines. In this paper, a portable solar-powered vaccine carrier box based on the Peltier Effect for effective vaccine cooling is designed and experimented with. The system has four 12V DC 3.5A Thermoelectric Cooler (TEC) Peltier Modules, with strategically positioned heat sinks outside the cooling box. Additionally, the setup incorporates a 12V 180W Solar Panel for daytime power generation and eight Rechargeable Li-Po 3.7V 4500mAh batteries for uninterrupted operation for 3 hours. The experimentation involved three distinct conditions. Firstly, a gradual temperature decrease was observed in the empty cooling box. Secondly, with the introduction of 10 sterile glass vaccine tubes filled with water, the temperature decreased more slowly, reaching 15°C in 62 minutes and 8°C after 90 minutes. Lastly, with 6 vaccine tubes, it took approximately 55 minutes to reach 15°C and about 90 minutes to achieve the desired 8°C temperature. The system exhibits a Coefficient of Performance of 0.42. The results emphasize the efficient cooling performance of the developed storage system, highlighting its capability to maintain temperatures below 15°C, a critical factor in preserving vaccines.
Solar Monitoring and Controlling Application using Light Dependent Resistor
Highly efficient solar panel operations are possible via a real-time sun-tracking orientation, which always allows and rotates the panels to the sun’s direction. Even though conventional methods trace the sun with microcontrollers and LDR sensors, they perform the function with minimal effectiveness because of low sensitivity and light interference. This paper introduces a new method that couples the principles of sensor systems and image processing, in which an automatic solar tracker is designed. Through using image processing software, the system combines processed sun images with sensor data to bring an effective functionality for solar panel self-orientation. Considering both hardware and system software, this approach is better for the photo voltaic power in terms of reliability and efficiency. It can carry out this task perfectly; it can simply manage different panels within a power plant, while on the other hand, it can ensure the best utilization of energy. Whereas microcontrollers are now being employed and sensors with limited effectiveness as measured by LDR are pervasively used, our proposed method employs the interaction of LDR sensors and image processing in a well-organized and effective manner. Unlike previous systems, this intelligent device provides an improvement on the solar tracking method through the real-time combination with data from sensors and sun images, presenting the flawless guidance of a vast number of solar panels in solar power stations.
A Cloud Edge Collaboration of Food Recognition Using Deep Neural Networks
Deep neural network-based learning methods are commonly used for classifying images or object detection with excellent performances. In this paper, we looked at how effective a deep convolution neural network (DCNN) is to identify food photography. Food identification is a sort of visual fine-grain recognition that is more difficult than traditional image recognition. Mobile apps in many countries have been omnipresent in many aspects of people's lives in the last few years. Fitting the healthcare potential of this pattern has become a focal point in the industry and researchers’ basic applications for architecture that patients should use in their well-being, prevention or treatment method. Mobile cloud computing has been introduced as a possible mobile well-being paradigm interoperability problems management service in various information formats. In this paper, I am integrating deep neural networks with cloud architecture to avoid substantial memory loss in mobile devices or web platforms where I can upload images, and it will predict the actual images and the names of food categories. The datasets I will be using are UECFOOD101; I will be running my deployment on the system as well as on the cloud for retrieving the data easily on mobile phones and web pages. The cloud architecture helps me offload the data, which is not required using computational offloading and profiling.
Comparative Analysis of Logistic Regression, LSTM, and Bi-LSTM Models for Sentiment Analysis on IMDB Movie Reviews
This study examines the efficacy of three distinct machine learning models for sentiment analysis: Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), and Logistic Regression. One of the most important tasks in natural language processing is sentiment analysis, which entails categorizing reviews as positive or negative. Fifty thousand sentiment-labeled movie reviews make up the dataset. To determine the most precise and effective technique for sentiment categorization, we put these models into practice and evaluated their respective performances. The highest accuracy of 89.42% is achieved by Logistic Regression, with precision of 88.35%, recall of 91.01%, and F1-score of 89.66%. The LSTM model, well-known for capturing temporal dependencies in text, obtained an accuracy of 86.23%, precision of 89.60%, recall of 82.22%, and an F1-score of 85.75%. The Bi-LSTM model, which processes input sequences in both forward and backward directions to better capture context, demonstrated accuracy of 87.65%, precision of 88.67%, recall of 86.54%, and an F1-score of 87.60%. The results show that although Logistic Regression performs better in accuracy, the Bi-LSTM model offers a substantial trade-off between precision and recall, making it a viable option for sentiment analysis applications. Despite its minor accuracy lag, the LSTM model provides important insights into the sequential relationships of the text data.
Non-Intrusive Load Identification of Residential Appliances Using Improved Dictionary Learning Technique
With the advent of time, the demand for power in the residential sector is increasing. Along with supply-side management, the demand side is also used to balance electricity and supply demand. To apply different demand-side management techniques, the energy disaggregation on metered data is used to retrieve information related to available demand. Non-intrusive load monitoring is a technique that separates the total power consumption into appliance loads with minimum invasion of privacy. Non-intrusive load monitoring covers the methods of Stochastic finite state machines, Neural Networks and Sparse Coding. Developing an efficient algorithm for NILM is a key challenge in maximizing energy conservation. Recently, a new deep learning technique called dictionary learning has been developed for energy disaggregation. Smart meters provide the whole house data, and the Dictionary technique is trained to predict an appliance's power or ON/OFF based on its power consumption. This research proposes the event-based dictionary learning technique, which can disaggregate multiple appliances through orthogonal matching pursuit (OMP) and kernel-singular value decomposition (K-SVD). The sparse matrix is predicted through OMP, and K-SVD predicts the dictionary matrix. The training, testing and validation are done on the ECO dataset. The results of this research are noticeable and show the validity of the proposed methodology for energy disaggregation.
Copyright (c) 2023 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
IoT-Based Smart Stick for Visually Impaired People
This paper presents the development of a smart stick to enhance visually impaired individuals' mobility, safety, and independence. The study aimed to integrate various features into a cohesive and user-centric device. The stick incorporates GSM/GPS tracking for continuous user location monitoring. This smart home automation mechanism allows the user to turn utilities ON/OFF with a single button press, obstacle detection using ultrasonic sensors, battery optimization techniques including solar charging and voltage supply to the battery by use of a stepper motor, emergency communication through GSM technology, and stick tracking within a certain radius using RF modules. The study's findings demonstrate the successful integration and functionality of the proposed features, cover the gaps identified with the help of extensive research and critical literature review and extend a gateway into the ocean of future study and diverse feature employment that could be incorporated in the smart stick.