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
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.
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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.
IoT Based Smart Helmet for Unsafe Event Detection for Mining Industry
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.
Computing the Demand Response in a Renewable Dominated Power System
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.
Home Automation for Disable Person Using Gesture Control and Artificial Intelligence
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.
Development of Cloud-based Water QualityMonitoring System
Water quality is paramount for sustaining life and maintaining ecological balance. However, traditional monitoring methods often must improve by providing real-time and comprehensive information. The developed systems show a cloud-based water quality monitoring system that overcomes the drawbacks of conventional approaches. The system allows numerous users to collect, store, and retrieve real-time data by combining sensors, an ESP32 microcontroller, Google Firebase Cloud, and a mobile application. The prototype illustrates the viability of using cloud computing to monitor water quality accurately and thoroughly. This effort advances the field by highlighting water quality is importance to supporting ecosystems and life. It highlights the system's contribution to allowing proactive decision-making and quick solutions to water quality challenges while outlining potential directions for future advancements in sensor calibration, testing in various water bodies, and cutting-edge data analytics methods. The cloud-based system for monitoring water quality has uses in various fields, such as environmental management, public health, and water resource conservation. It makes it easier to make educated decisions and take preventative action to preserve water quality and sustainability.
Segregation of Quality Products on the Production Line
In the highly competitive manufacturing industry, producing high-quality products is crucial for success and reputation. The issue of segregating quality products on the production line has emerged as a critical concern, necessitating effective strategies to tackle that problem. This project aims to design a quality control unit using controllers, sensors, and a robotic arm to segregate products based on height, substance amount, and cap presence. Ensuring product quality enhances customer satisfaction, improves production efficiency, optimizes resources, and boosts profitability through waste reduction and brand preservation. Workforce training will focus on quality standards and defect identification.
Investigation into the Development of Waste to Energy as an Alternative Energy Source
Pakistan, a developing nation, is facing a critical crisis regarding its fossil fuel resources and the production of electrical energy. The country's electricity demand has reached approximately 29,000 MW, while the generation capacity is only 22,000 MW. This significant gap between generation and demand has led to load-shedding. To address this issue, we are considering the development of waste-to-energy plants, which are waste management facilities that utilize combustion to generate electricity. Instead of relying on traditional fossil fuels like coal, oil, or natural gas, waste-to-energy plants use trash as a fuel source. By burning this fuel, heat is produced, which heats water to generate steam that drives a turbine, ultimately creating electricity. While waste-to-energy is often portrayed as a viable method for extracting energy from available resources, it does pose challenges to the circular economy. This approach generates toxic waste, contributes to air pollution, and exacerbates climate change. These plants emit chemicals such as mercury and dioxins, which pose risks to human and environmental health. To address these concerns, we aim to investigate the development of waste-to-energy as an alternative energy source while prioritizing creating a healthy environment. As part of this effort, we intend to implement a sensor network to detect the heat generated during incineration and monitor the emission of pollutants. Our overarching goal is to generate electricity while recycling waste materials as much as possible, thus promoting a sustainable and eco-friendly approach.
Implementation and Fabrication of Hybrid Solar Inverter
Inverters are frequently utilized in home and industrial settings to act as an alternative source of electricity in case the utility network's electrical supply is interrupted. However, due to the low capacity of the battery, the inverter was shut down for the heavy-load appliances. This endeavour is constructed in a way that uses solar energy to get around this restriction. An inverter powered by a battery makes up the hybrid inverter with a solar battery charging system. It incorporates maximum power point tracking (MPPT) to extract maximum power from the solar panels and efficiently charge the batteries. With the assistance of driver circuitry and a transformer, this inverter can generate up to 230V. The solar power source itself and the grid power supply are used to charge the battery. If the solar power supply is available, the relay circuitry uses the solar power to supply the load. Otherwise, the load connects to the grid power supply. The battery is also charged by this solar power source to be used as a backup in the future. When solar power is unavailable, charging the battery with the main supply is a pleasant option. As a result, this inverter may last longer and give the consumer an uninterrupted power supply.
Design and Implementation of Secure Electronic Voting System Using Fingerprint Biometrics
Fingerprint-Based Voting Project is an application that recognizes users based on their fingerprints. Each person has a different finger pattern, so you can easily authenticate voters. This system allows voters to vote using their fingerprints. Fingerprints are used to identify users uniquely. As the fingerprint minutiae features differ for each human being, the fingerprint is utilized to authenticate the voters. Voters can only vote once for a candidate. The system will not allow the voter to vote for a second time. The system allows administrators to add names and candidate photos of candidates nominated for election. The administrator only has the right to add the names and photos of nominated candidates. The admin will verify voters and register voter names. Administrators authenticate users by verifying their identities and then enrolling voters. Users can log in and vote for candidates after receiving a user ID and password from the administrator. The system allows the user to vote for only one candidate for a particular election. The voter and the admin can view the election results using the election ID. Voting results will be updated immediately. This study shows that the proposed web-based voting system is fast, efficient and fraud-free.