Journal of Artificial Intelligence and Computing
type: channel
description: 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.
publisher: Swedish College of Engineering and Technology
language: en
publication name: Journal of Artificial Intelligence and Computing
issn: 3005-3358
items: IoT Forensics and Current Landscape and Future Trajectories: A Survey
items: Digitization of Enterprises: Assessing the Adoption and Impact of Digital Tools in Small and Medium Enterprises in Gilgit-Baltistan
items: Leveraging Transformer-Based Natural Language Processing for Adaptive Language Learning in Digital Humanities Environments
items: 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
items: Cybersecurity Challenges, Vulnerabilities, and Emerging Security Solutions in the Internet of Things
items: The Overwhelming Applications of Artificial Intelligence in Every Field of Life: A Comprehensive Review
items: Towards Resilient Smart Cities: Security, Trust, and Sustainability in Edge Computing - A Survey
items: Future of Artificial Intelligence with the Perspective of Deep Learning: A Review
items: Analysis of an Anti-Aliasing and Reconstruction System
items: Smart Grid Innovation: Machine Learning for Real-Time Energy Management and Load Balancing
items: Design and Implementation of IoT-Based Smart Meter
items: Future of Programmable Logic Controller, Recent Trends, and Challenges
items: Systematic Study of Anti-Jamming Strategies Using Direct Sequence Spread Spectrum
items: Why SCADA: Fundamentals, Challenges and Future
items: Optimization of Wire Electrical Discharge Machining Parameters for Inconel Alloys: A Comprehensive Review of Hybrid Techniques
items: Deep Learning for Predictive Maintenance in Smart Manufacturing — A Review
items: Nanofluids in Metal Cutting and Grinding: A Comprehensive Review
items: Review on Surface Modification and Coating Effects in Wire EDM Machining Performance
items: Design and Implementation of a Home Appliance Automation Framework
items: Harnessing Industrial Revolution Using Artificial Intelligence for Robust Economic Corridors and Sustainable Development
items: Blood Cell Disease Prediction with Machine Learning
items: Design and Experimentation of Peltier-Based Thermoelectric Cooling Vaccine Carrier Box
items: Solar Monitoring and Controlling Application using Light Dependent Resistor
items: A Cloud Edge Collaboration of Food Recognition Using Deep Neural Networks
items: Comparative Analysis of Logistic Regression, LSTM, and Bi-LSTM Models for Sentiment Analysis on IMDB Movie Reviews
items: Non-Intrusive Load Identification of Residential Appliances Using Improved Dictionary Learning Technique
items: IoT-Based Smart Stick for Visually Impaired People
items: IoT Based Smart Helmet for Unsafe Event Detection for Mining Industry
items: Computing the Demand Response in a Renewable Dominated Power System
items: Home Automation for Disable Person Using Gesture Control and Artificial Intelligence

IoT Forensics and Current Landscape and Future Trajectories: A Survey
type: item
description: 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.
subject: Articles
  topic: section
subject: Evidence
  topic: keywords
subject: digital investigation
  topic: keywords
subject: cybercrime
  topic: keywords
subject: forensic
  topic: keywords
subject: IoT Security
  topic: keywords
subject: IoT privacy
  topic: keywords
subject: Forensic Readiness
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Kamran Ahmad
creator: Irshad Ahmed
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 19
ending page: 27
doi: 10.57041/n7qj7v96

Digitization of Enterprises: Assessing the Adoption and Impact of Digital Tools in Small and Medium Enterprises in Gilgit-Baltistan
type: item
description: 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.
subject: Articles
  topic: section
subject: Small and medium enterprises
  topic: keywords
subject: digitalization
  topic: keywords
subject: statistical analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Haffiz_Ud_Din
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 28
ending page: 35
doi: 10.57041/q8bggn84

Leveraging Transformer-Based Natural Language Processing for Adaptive Language Learning in Digital Humanities Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Transformer models
  topic: keywords
subject: natural language processing
  topic: keywords
subject: digital humanities
  topic: keywords
subject: adaptive learning
  topic: keywords
subject: computational linguistics
  topic: keywords
subject: e-learning technology
  topic: keywords
subject: corpus linguistics
  topic: keywords
creator: Saad Rehman Babary
creator: Ramsha Khalid
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 36
ending page: 43
doi: 10.57041/b5b3x793

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
type: item
description: 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…
subject: Articles
  topic: section
subject: Generative artificial intelligence
  topic: keywords
subject: large language models
  topic: keywords
subject: automated written corrective feedback
  topic: keywords
subject: EFL academic writing
  topic: keywords
subject: learner engagement
  topic: keywords
subject: natural language processing in education
  topic: keywords
subject: computer-assisted language learning
  topic: keywords
creator: Mujtaba Kamal Pasha
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-29
publication date: 2026-06-29
starting page: 8
ending page: 18
doi: 10.57041/x2hg0114

Cybersecurity Challenges, Vulnerabilities, and Emerging Security Solutions in the Internet of Things
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IOT)
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Vulnerabilities
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Authentication
  topic: keywords
subject: Blockchain
  topic: keywords
creator: Eeman Khokhar
creator: Irshad Ahmed
rights: Copyright (c) 2026 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-29
publication date: 2026-06-29
starting page: 1
ending page: 7
doi: 10.57041/jjfgdk93

The Overwhelming Applications of Artificial Intelligence in Every Field of Life: A Comprehensive Review
type: item
description: 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 al…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: machine laerning
  topic: keywords
subject: deep learning
  topic: keywords
subject: neural network
  topic: keywords
subject: natural language processing
  topic: keywords
subject: computer vision
  topic: keywords
subject: robotics
  topic: keywords
subject: intelligent systems
  topic: keywords
subject: automation
  topic: keywords
subject: smart technologies
  topic: keywords
creator: Guangson Wang
creator: Wang Xia
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 21
ending page: 28
doi: 10.57041/hf93dn74

Towards Resilient Smart Cities: Security, Trust, and Sustainability in Edge Computing - A Survey
type: item
description: 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.
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: smart cities
  topic: keywords
subject: edge security
  topic: keywords
subject: sustaibility
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: blockchain
  topic: keywords
subject: trusted execution environment
  topic: keywords
subject: federated learning
  topic: keywords
subject: privacy preservation
  topic: keywords
subject: post-quantum cryptography
  topic: keywords
creator: Areeb Imtar Chaudary
creator: Irshad Ahmed Sumra
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 15
ending page: 21
doi: 10.57041/p5ypkz62

Future of Artificial Intelligence with the Perspective of Deep Learning: A Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: deep learning
  topic: keywords
subject: neural network
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Edge computing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Asanka Sayakara
creator: Zaid Khan
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 11
ending page: 14
doi: 10.57041/4ammg320

Analysis of an Anti-Aliasing and Reconstruction System
type: item
description: 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.
subject: Articles
  topic: section
subject: Electrocardiogram (ECG)
  topic: keywords
subject: low-pass filter
  topic: keywords
subject: aliasing
  topic: keywords
subject: noise
  topic: keywords
creator: Mashal Ahmad
creator: Zhong Wang
creator: Fakhar Sial
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 7
ending page: 10
doi: 10.57041/s4jfnh46

Smart Grid Innovation: Machine Learning for Real-Time Energy Management and Load Balancing
type: item
description: 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.
subject: Articles
  topic: section
subject: Smart grid
  topic: keywords
subject: machine laerning
  topic: keywords
subject: load balancing
  topic: keywords
creator: Talha Zafar
creator: Muhammad Abdullah
creator: Zain Siddiqui
creator: Muneeb
creator: Hussain Shah
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 6
doi: 10.57041/434ghm74

Design and Implementation of IoT-Based Smart Meter
type: item
description: 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.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: smart meter
  topic: keywords
subject: power
  topic: keywords
creator: Ayeza Khan
creator: Ifra Shabbir
creator: Shoaib Zafar
creator: Jalal Haider
creator: Afifah Tahir
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 26
ending page: 30
doi: 10.57041/q1bvxd17

Future of Programmable Logic Controller, Recent Trends, and Challenges
type: item
description: 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.
subject: Articles
  topic: section
subject: PLC
  topic: keywords
subject: CPU
  topic: keywords
subject: SCADA
  topic: keywords
subject: HMI
  topic: keywords
creator: Muhammad Husnain Shahid
creator: Muhammad Hammad Tahir
creator: Asma Arshad
creator: Arooj Abbas
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 20
ending page: 25
doi: 10.57041/35daws31

Systematic Study of Anti-Jamming Strategies Using Direct Sequence Spread Spectrum
type: item
description: 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.
subject: Articles
  topic: section
subject: Spread spectrum
  topic: keywords
subject: DSSS
  topic: keywords
subject: FHSS
  topic: keywords
subject: processing gain
  topic: keywords
creator: Rizwan Ali
creator: Muhammad Farooq
creator: Malaika Basit
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 15
ending page: 19
doi: 10.57041/2qb8qs09

Why SCADA: Fundamentals, Challenges and Future
type: item
description: 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.
subject: Articles
  topic: section
subject: SCADA
  topic: keywords
subject: IIoT
  topic: keywords
subject: CAGR
  topic: keywords
creator: Raja Talal Hassan
creator: Bashaar Saleem
creator: S.A. Muntazir Mehdi
creator: Shaharyar Khan
creator: Abdul Hanan
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 11
ending page: 14
doi: 10.57041/1pcykp08

Optimization of Wire Electrical Discharge Machining Parameters for Inconel Alloys: A Comprehensive Review of Hybrid Techniques
type: item
description: 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.
subject: Articles
  topic: section
subject: Wire Electrical Discharge Machining (WEDM);
  topic: keywords
subject: Material Removal Rate (MRR);
  topic: keywords
subject: Surface Roughness (SR);
  topic: keywords
subject: Response Surface Methodology (RSM);
  topic: keywords
subject: Artificial Neural Network (ANN);
  topic: keywords
subject: Genetic Algorithm (GA);
  topic: keywords
creator: Deepak kumar
creator: Dr.Pawan kumar
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 33
ending page: 39
doi: 10.57041/n1jxqt14

Deep Learning for Predictive Maintenance in Smart Manufacturing — A Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Predictive maintenance,
  topic: keywords
subject: Deep learning,
  topic: keywords
subject: smart manufacturing,
  topic: keywords
subject: Anomaly detection,
  topic: keywords
subject: Industry 4.0.
  topic: keywords
creator: Ravi Tyagi
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 15
ending page: 19
doi: 10.57041/nv82e636

Nanofluids in Metal Cutting and Grinding: A Comprehensive Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Nanofluids
  topic: keywords
subject: Metal Cutting
  topic: keywords
subject: Grinding
  topic: keywords
subject: Sustainable Manufacturing
  topic: keywords
subject: Tool Wear
  topic: keywords
subject: Surface Roughness
  topic: keywords
subject: Hybrid Nanofluids
  topic: keywords
creator: Ravi Tyagi
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-12
publication date: 2025-10-12
starting page: 1
ending page: 10
doi: 10.57041/sjwa5k55

Review on Surface Modification and Coating Effects in Wire EDM Machining Performance
type: item
description: 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.
subject: Articles
  topic: section
subject: Wire EDM
  topic: keywords
subject: Coated Electrodes
  topic: keywords
subject: Surface Modification
  topic: keywords
subject: Machining Performance
  topic: keywords
subject: Surface Integrity
  topic: keywords
creator: Ravi Tyagi
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 25
ending page: 32
doi: 10.57041/288j8206

Design and Implementation of a Home Appliance Automation Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: Automation
  topic: keywords
subject: GSM
  topic: keywords
subject: Android Application
  topic: keywords
subject: Arduino
  topic: keywords
subject: Home Appliances
  topic: keywords
subject: Smart Phones
  topic: keywords
subject: Short Messaging Service (SMS)
  topic: keywords
creator: Arooj Aslam
creator: Aroosa Aslam
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 6
ending page: 14
doi: 10.57041/pqnp3e63

Harnessing Industrial Revolution Using Artificial Intelligence for Robust Economic Corridors and Sustainable Development
type: item
description: 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.
subject: Articles
  topic: section
subject: Industrial revolution
  topic: keywords
subject: autonomous truck platooning
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: performance modelling
  topic: keywords
creator: Abdullah Addas
creator: Fawad Naseer
creator: Usman Khalid
rights: Copyright (c) 2025 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 1
ending page: 5
doi: 10.57041/0g1syp46

Blood Cell Disease Prediction with Machine Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Blood Cell
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: Blood cell Disease
  topic: keywords
creator: Ateeqa Arshad
creator: Arooj Fatima
creator: Asad Tahir
creator: Rida Fatima
creator: Mudasira Khalil
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-28
publication date: 2024-06-28
starting page: 29
ending page: 32
doi: 10.57041/4kbvf380

Design and Experimentation of Peltier-Based Thermoelectric Cooling Vaccine Carrier Box
type: item
description: 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.
subject: Articles
  topic: section
subject: Thermoelectric Cooler
  topic: keywords
subject: Peltier Effect
  topic: keywords
subject: Coefficient of Performance
  topic: keywords
subject: Heat Sink
  topic: keywords
subject: Solar Panel
  topic: keywords
subject: Cold Chain System
  topic: keywords
creator: Yousaf Khan
creator: Taimur Ahmed Khan
creator: Muhammad Ilyas
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-28
publication date: 2024-06-28
starting page: 23
ending page: 28
doi: 10.57041/cwmf6262

Solar Monitoring and Controlling Application using Light Dependent Resistor
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: neural network
  topic: keywords
subject: image processing
  topic: keywords
creator: Khaliq Ahmed
creator: Asif Raza
creator: Kashif Sheikh
creator: Osama Ahmed Siddique
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-28
publication date: 2024-06-28
starting page: 19
ending page: 22
doi: 10.57041/mms2rs57

A Cloud Edge Collaboration of Food Recognition Using Deep Neural Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: neural network
  topic: keywords
subject: image processing
  topic: keywords
creator: Muhammad Talha Khan
creator: Muhammad Hassan Khan
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 9
ending page: 18
doi: 10.57041/g4zmgy34

Comparative Analysis of Logistic Regression, LSTM, and Bi-LSTM Models for Sentiment Analysis on IMDB Movie Reviews
type: item
description: 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.
subject: Articles
  topic: section
subject: LSTM
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: Logistic Regression
  topic: keywords
subject: NLP
  topic: keywords
creator: Mureed Hussain
creator: Mudasser Naseer
rights: Copyright (c) 2024 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-28
publication date: 2024-06-28
starting page: 1
ending page: 8
doi: 10.57041/6t2gfw23

Non-Intrusive Load Identification of Residential Appliances Using Improved Dictionary Learning Technique
type: item
description: 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.
subject: Articles
  topic: section
subject: Dictionary learning
  topic: keywords
subject: non-intrusive load monitoring
  topic: keywords
subject: residential load
  topic: keywords
subject: sparse representation
  topic: keywords
creator: Sundas
creator: Intisar Ali Sajjad
rights: Copyright (c) 2023 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 30
ending page: 36
doi: 10.57041/xm49bx49

IoT-Based Smart Stick for Visually Impaired People
type: item
description: 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.
subject: Articles
  topic: section
subject: Battery Optimization
  topic: keywords
subject: GPS
  topic: keywords
subject: GSM
  topic: keywords
subject: IoT
  topic: keywords
subject: Obstacle Detection
  topic: keywords
subject: Solar
  topic: keywords
subject: Smart Home
  topic: keywords
subject: Automation
  topic: keywords
creator: Hamza Usman
creator: Vareesha Shakeel
creator: Usman Atif
creator: Usman Khan
creator: Laiba
rights: Copyright (c) 2023 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 18
ending page: 29
doi: 10.57041/4cmmzd66

IoT Based Smart Helmet for Unsafe Event Detection for Mining Industry
type: item
description: The major goal of this project is to design a system that could detect and communicate with the control room in case of an emergency. Methane, CarbonMonoxide and Hydrogen Sulfide gases are hazardous for humans and are present in higher concentrations in mines. This system will help to overcome this problem and help to detect the gases at an early stage. Coal mining can cause several adverse effects on the environment. For instance, Toxic gases may be released into the air, causing suffocation, gas poisoning, roof collapse, and gas explosions. Considering all these viewpoints, the designed system isIoT Based Smart Helmet for Unsafe Event Detection for Mining Industry for monitoring abnormal conditions. Some of the abnormal conditions include the leaking of hazardous gases and rise/fall in temperature and humidity levels. After detecting gases, the next essential step is communicating the collected sensor data to the control room using an appropriate wireless communication module. The features in our system increase the life expectancy of the coal miners by warning them about danger using a buzzer alarm and giving the information to the control room for immediate help.
subject: Articles
  topic: section
subject: Smart helmet
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: detection
  topic: keywords
subject: mining industry
  topic: keywords
subject: wireless fidelity
  topic: keywords
creator: Ali Hamza
creator: Muhammad Haris Rauf
creator: Muhammad Hashir
creator: Muhammad Kounain
rights: Copyright (c) 2023 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 13
ending page: 17
doi: 10.57041/x8d9kq98

Computing the Demand Response in a Renewable Dominated Power System
type: item
description: Demand response refers to action where consumers can significantly optimize the electricity grid by reducing or shifting power consumption during peak periods using time-based rates or economic incentives. In this project, we have proposed a system where a small-scaled distributed generation based on renewable energy, including solar energy, with their coordination control, has been modelled to ensure generation solvency and a demand response strategy based on load shifting technique using a demand response system (DRS) for demand-side proficiency.
subject: Articles
  topic: section
subject: Computing
  topic: keywords
subject: power demand
  topic: keywords
subject: power generation
  topic: keywords
subject: power systems
  topic: keywords
subject: renewable energy
  topic: keywords
creator: Muhammad Moiz
creator: Muhammad Moeez
creator: Saad Shakeel
creator: Waseem Akram
rights: Copyright (c) 2023 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-20
publication date: 2023-12-20
starting page: 6
ending page: 12
doi: 10.57041/4t741a34

Home Automation for Disable Person Using Gesture Control and Artificial Intelligence
type: item
description: For an extended time, gestures communique has performed a giant function in conveying information. Before the evolution of human beings, while no verbal mode of communique changed into the present, gesture communique changed into the simplest manner to interact with different human fellows. After evolution, with language improvement, the want of gestures for brief variety communique changed into removed. However, it became nonetheless giant in conveying messages at large distances, i.e., by lightening the hearth place at a far-off place. The modern generation is gaining this mode of communique, and it has correctly translated those gestures into the virtual world. The improvement of augmented truth is an instance of it. With the arrival of deep gaining knowledge of algorithms like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the issue in records evaluation and sample recognition has come to be very smooth and robust. In this undertaking, we've advanced a mind gestures (signals) reputation machine that controls home automation home equipment for bodily impaired people.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: recurrent neural network
  topic: keywords
subject: signals
  topic: keywords
creator: Mureed Hussain
rights: Copyright (c) 2023 https://grsh.org/journal1/index.php/jaic/cr https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 1
ending page: 5
doi: 10.57041/2czdnq93