NUML International Journal of Engineering and Computing
NUML International Journal of Engineering and Computing (NIJEC) is an Open Access research journal published by the Faculty of Engineering and Computing - National University of Modern Languages (NUML). NIJEC was started in 2021 with objective of disseminating high quality original research work in the field of Computer Sciences, Electrical Engineering, Software Engineering and Mathematics. All submissions to NIJEC are processed through rigorous screening and Double Blind Peer-Review processes. The submissions are reviewed by at least one national and one international reviewer with strong academic and research background in their areas of expertise. NIJEC is published biannually, both in soft and hard form. It has a wide circulation nationally and internationally. All accepted papers are published online on the journal’s website.
National university of Modern Languages, Islamabad.
Cybersecurity Awareness and Preparedness of Academic Library Professionals in Pakistan: Bridging the Training Gap in Digital Libraries
The digitalization of academic libraries has increased cybersecurity issues, which has led to a greater need for professional preparedness. The study aims to focus on the awareness of academic library professionals about secure practices, their perceived risk factors, availability and support of training, and institutional support in Pakistan. The research adopted a quantitative cross-sectional survey study, which involved 101 respondents holding professional jobs within the private and public institutions in the institutions of higher learning and the study used descriptive and inferential statistics to analyze the data. The outcomes reveal that the overall percentage of cybersecurity awareness (M = 3.55) and secure practices adoption (M = 3.90) is high. However, the absence of even the most basic deficiencies was identified in the professional competence and formal training because only a third of respondents obtained systematic training on cybersecurity. Lack of systematic training was also among the severe weaknesses, whereas the institutional support rating was rated very highly (M = 4.06). These results show that a critical disconnect exists between good individual practicing levels on cybersecurity and a low level of institutionally built training infrastructure. This underscores that there is a pressing need to spearhead the capacity building of cybersecurity policies in libraries in an organized manner.
Copyright (c) 2026 Sahar Afshan, Maryam Naveed, Hassan Shah, Sobia Ayaz
The Design of Epi-RPNN for the Analysis of Bacteriophage Infection Model
In this study, we examine the analysis with accuracy based on intelligent computing for the bacteriophage infection model frequently employed in epidemiology. The microbiological is an interesting phenomenon known as bacteriophage infection or phage infection. Bacteriophages are viruses that target and infect bacteria specifically, then use the bacteria as hosts for their own replication. By injecting their genetic material into the bacterial cell, these phages cause the host cell’s machinery to be redirected in order to produce more phages, which ultimately causes the lysis or obliteration of the bacterial host. The derivation of the basic reproduction number and numerical simulations are conducted through a new machine learning approach called the random projection neural network (RPNN) method. The accuracy and robustness of our methodology are examined through a comparison of the results with numerical solvers ode23t and ode15s available in MATLAB. Moreover, the data testing, training and validation of mean square error are examined through performance, training test, error histogram, regression and fitness plots. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data has been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
Copyright (c) 2026 Sabir Ali, Shahzad Khattak, Waseem, Faiza, Sabra Hafeez
A Systematic Literature Review on Modern Cryptographic and Authentication Schemes for Securing the Internet of Things
The rapid integration of the Internet of Things (IoT) into healthcare ecosystems has revolutionized patient monitoring and data accessibility; however, it has simultaneously expanded the cyber-attack surface, leaving sensitive medical data vulnerable to sophisticated breaches. This systematic literature review (SLR) addresses the critical challenge of balancing high-level security with the severe resource constraints of medical sensors and edge devices. By synthesizing evidence from 80 high-impact studies including 18 primary research articles published between 2022 and 2025 this paper evaluates the quality and efficacy of emerging cryptographic frameworks. The methodology utilizes a rigorous quality assessment framework to categorize research into "Strong," "Moderate," and "Weak" tiers. Key findings reveal a significant paradigm shift toward lightweight symmetric ciphers, such as GIFT and PRESENT, and certificateless authentication protocols like ELWSCAS, which reduce communication overhead in narrow-band environments. The analysis further explores the role of blockchain-assisted decentralization and DNA-based encryption in mitigating Single Point of Failure risks and providing high entropy. While decentralized models significantly enhance data integrity, they frequently encounter a scalability wall regarding transaction latency. Furthermore, the review assesses quantum readiness, noting that while lattice-based standards are being ported to microcontrollers, memory footprints remain a barrier for simpler sensors. Ultimately, this SLR maps the current technical frontiers and provides a strategic roadmap for future research, emphasizing the transition toward lightweight, quantum-resistant architectures as the next essential step in securing the global healthcare IoT infrastructure. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data has been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
Copyright (c) 2026 Tehseen Hussain, Fraz Ahmad, Dr. Zia Ur Rehman
A Review of Real-Time Deep Learning–Based Object Detection Models for Resource-Constrained Embedded Systems
Computer vision relies heavily on object detection; from autonomous drones to industry monitoring it is everywhere. However, despite these advances when one wants to implement these cutting-edge object detection models in embedded system it proves to be difficult due to the limitations in processing power, memory and energy consumption. A detailed examination of real-time object detection models designed for resource-constrained devices is presented in this paper. We investigate widely used one stage detectors like SSD (Single Shot Multi Box) and YOLO (You Only Look Once). In addition, we also discuss model compression methods like knowledge distillation, pruning and quantization, which enable efficient deployment on embedded systems. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data have been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
The Effect of Dimensionality Reduction on Machine Learning Models Performance: A Review
The performance of Machine learning models are fully dependent on the training data. In today’s age where many companies transforming their businesses towards digitalization. A huge volume of raw data is generated and collected, extracting important and useful features from raw data is quite a big challenge, because it is computationally very expensive and time-consuming to train machine learning models on raw data and result in poor model’s performance due to irrelevant features. So, it is necessary to filter out data by reducing the size of features or dimensions and extracting important features from a huge amount of data before implementing any machine learning model on it. In this situation, Dimensionality reduction techniques come into play to eliminate unnecessary or irrelevant features from the data set. In this paper, we are going to review some of the important dimensionality reduction techniques on different machine learning models and evaluate the performance of how dimensionality reduction techniques improve the overall performance of models. After reviewing the research work of different authors, we conclude our results by comparing different implemented techniques of dimensionality reduction, the results before reducing the size of features/dimensions, and after implementing dimensionality reduction techniques and figure out key findings and limitations of reviewed papers. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data have been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
Copyright (c) 2026 Inam Ul Haq, Toffiq Saddique, Saqlain Abbas, Wasi Haider Butt, Umar Ajaib Khan
Vehicle detection and tracking using kalman filter and hungarian algorithm for driving assistance in VANET’s
In today’s world, the huge increase in automobile vehicles counts on the roads in both rural and urban areas have turned out to create large number of issues which result in the administering and governing of the vehicle on the roads and highways for better driving assistance. Vehicle identification and monitoring by using the information collected from transport monitoring system leads to a defining standard approach for the complex transportation system. In our proposed research Kalman filtering and Hungarian approach used for the identification and tracking of vehicles which mainly focuses on moving vehicles in context of vehicular ad-hoc networks (VANET’s). This paper illustrates the identifying and monitoring the multiple vehicles using deep learning for driving assistance. Kalman filtering is used for monitoring the objects with the pre-trained COCO dataset. Pipeline simplifies the object detection by producing the bounding box around the vehicle. This approach quickly detects the moving vehicle from the running video with the bounding box. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data have been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
Copyright (c) 2026 Muhammad Tahir, Rao Umer Farooq, Kaleem Akram
An Information Processing Model for Assessment of User Reviews
This Analysis of reviews became a valuable source of accurate information. In this research, we will analyze reviews. Analyzed reviews will create an accurate data set. Fitting an appropriate model is necessary to get accuracy. Problems in fitting an appropriate model are under-fitting and over-fitting. An under-fit model will be less flexible and cannot account for the data. Over-fitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. To solve the mentioned problems, features and appropriate algorithms are selected. As a solution, we will perform preprocessing in a better way with the machine learning algorithms to assess the impact of preprocessing. The main focus of this research is to assess the impact of preprocessing steps on different classifiers.
Copyright (c) 2025 Hamid Hamid, Naila Batool, Muhammad Tahir, Muhammad Usman
A Novel Framework for the Accuracy Enhancement of Facial Expression Recognition System
Facial Expression Recognition has become a promising field for more natural interactivity with computing devices and machines and has become the focus of attention for many research scholars over the past decade. Newly developed facial emotions recognition methods focus on neutral expression or six expressions used in most state-of-the-art methods. Accuracy is the main problem in the face recognition results. The problem that must be tackled is the optimization of the expression recognition algorithm i.e. to detect, isolate and correctly translate one of the major expressions of the human face with accuracy targeted towards 100%. This work will try to improve the accuracy of recognizing facial expression by using Histogram of Oriented Gradients (HOG) and Local Ternary Pattern (LTP).
Coefficient Inequalities for Certain New Class of Analytic Functions Related withq-starlike Exponential Function
The purpose of the present work is to define a class of star-like function with respect to symmetrical points in the domain of exponential functions related to the q-exponential functions. We will determine the possible upper-bound of the third Hankel determinant for the function q-starlike.
Digital Forensics in Action The Role of Digital Forensics in Cybercrime Investigations
The explosion of technology in recent years has revolutionized the face of cybercrime and requires new and innovative approaches to investigating digital crimes. In this research, we present the all-encompassing role of digital forensics in cybercrime investigations with a focus on methodologies, tools, and techniques for computer forensics, mobile forensics, network forensics, and IoT forensics solutions. With the steady growth of digital systems, and the integration of these systems, has come an increase in the importance of accurate and efficient forensic methods. This paper studies the effectiveness of current digital forensic strategies and presented the challenges faced while implementing them, i.e. encryption, anti- forensic measures, and the proliferation of digital forensic evidence. The key findings now include increasing dependence on specialist forensic software, the challenges of legality and jurisdiction, and the urgent necessity of global legislation and standards so that cross-border collaboration may be effective. The paper also looks at AI and ML in automating forensics and increasing the speed and accuracy of gathering evidence. By filling the current methodological gaps and examining the existing real-life cases, this study highlights the need to keep pace with innovation in digital forensics to counteract the ever growing cyber threats adequately.
Copyright (c) 2025 Hassan Shah Khan, Syed Muhammad Atif, Abeer Mustufa
DPSO Based Machine Learning-driven Approach for the Solution of PDEs: A Case Study of Burger Equation
Burger's equation represents a nonlinear second-order partial differential equation that is instrumental in characterizing the behavior of shock waves within the realm of gas dynamics. This research article examines the classical formulation of Burger's equation and its conversion into the linear heat equation. An artificial neural network (ANN) that is directed by the hybrid fractional order particle swarm optimisation (FO-PSO) technique is used to solve this partial differential equation (PDE). FO-PSO is a novel computer approach that finds optimal solutions by using a fractional order for the velocity of moving particles. By including optimised weights, the ANN framework is mainly used to reduce absolute errors (AE) in the approximation solutions. This research article's main goal is to solve the PDE using an ANN structure that was trained with the FO-PSO technique. The outcomes are contrasted with other modern methods to assess the efficacy of our methodology. Additionally, a supervised learning technique is implemented for the training, testing, and validation of our proposed outcomes, which are presented graphically. Conflict of interest: The authors have declared no potential conflicts of interest and falsification/fabrication of data with respect to the research, authorship, and/or publication of this article. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Copyright (c) 2025 Shahzad Khattak, Sabir Ali
Med Box: Smart Medication Reminding & Monitoring
Effective medication management plays a vital role in a patient's recovery, and timely adherence to prescribed medication is key to achieving positive health outcomes. In our fast-paced society, many individuals face challenges in maintaining their medication schedules, which can significantly impact their recovery process. This concern is particularly pronounced among older adults and those managing chronic conditions. Inadequate medication adherence not only threatens health but can also lead to severe complications. Medication management applications have surfaced, although there has been limited advancement in this area in Pakistan, with few hospitals adopting comprehensive medication management systems. There is also a notable gap in home-based solutions that can effectively track medication intake and maintain detailed medication histories. This study aims to evaluate a newly developed medication management system that enhances patient adherence while minimizing medication errors and providing a reliable record of medication history. The proposed prototype includes both a hardware component and a mobile application called "Med App," designed to support patients in managing their medications and documenting their medication journeys. The integrated hardware features five automated containers for medication storage, all operated seamlessly through the Med App. It leverages advanced sensors and image processing technologies to ensure precise medication dispensing and tracking within each container. All collected data is securely stored on the Firebase platform, integrating information from both the app and the hardware.
Copyright (c) 2025 Zahid ALI KHAN, Khuzama Nadeem , Ali Hassan
In the 21st century, particularly in regions like Pakistan, critical challenges such as water scarcity, rising gas costs, and the severe effects of climate change have become increasingly urgent. To tackle these issues, numerous IoT-based systems are being developed globally to promote efficient resource management and sustainability. However, many of the currently available IoT solutions, such as 1NCE's Smart Utilities Management, Semtech's LoRa® Technology for Smart Utilities, and Robustel's Utility Management Solutions, are often expensive. These systems require specialized and costly appliances for their functionality, making them less accessible to average users, especially in regions with limited resources [1]. In contrast, the IoT-based Smart Utility Control System offers a more affordable and accessible alternative. This system integrates seamlessly with a smartphone-based Android application, utilizing Firebase as its platform. By eliminating the need for expensive supportive appliances, it provides an efficient and cost-effective solution for managing utilities like water and energy. The system is designed to ensure convenience, promote resource conservation, and support households in addressing challenges related to resource scarcity and rising costs, particularly in economically vulnerable regions. With two key modules, our proposed solution addresses critical issues households in Pakistan and similar regions face. The first module wireless connectivity of water tanks focuses on water monitoring and control, offering real-time monitoring of water levels in main and supply tanks. In a country facing diminishing water resources, our system's ability to alert users to low water levels and remotely control water motors can help prevent wastage and ensure efficient water usage, thereby mitigating the effects of water scarcity. The second module monitors and controls the gas geyser for energy-efficient preheating and remote operation. Through the mobile app, users can adjust and schedule the operation of the geyser according to their preferences. This includes setting specific times for the geyser to switch on or off, optimizing energy usage, and ensuring hot water availability. By providing this level of control and flexibility, our system promotes energy efficiency and enhances user convenience and comfort.
Copyright (c) 2025 Khansa niazi, Waleed Ahmad, Muhammad Hassan Nadeem
Evolving Methods in Social Media Sentiment Analysis: Innovations and Challenges
Several major studies conducted during the period 2010-2023 have been compiled in this article to illustrate recent progress made toward improving sentiment analysis methods [1] applied to social media websites during that period. In light of the vast amount of user-generated content that is uploaded daily to social media platforms[2], [3] such as Facebook or Instagram, it is essential to evaluate public sentiment on these channels when marketing or monitoring public opinion processes. As a result, traditional machine learning models[4] cannot comprehend the unstructured and evolving language found on social media platforms. For the purpose of improving accuracy, researchers have developed ensemble learning techniques[5] as well as hybrid approaches [6]. CNNs, RNNs, and Transformer models [7] such as BERT have revolutionized sentiment analysis by revealing intricate details. An analysis of real-time sentiment in social media videos can be quickly performed using big data analytics. Multimodal methods [8], [9] incorporating visual, audio, and textual data provide a more comprehensive understanding of sentiments in social media videos. Model adaptability across datasets is improved through transfer learning. Unfortunately, differences in language and ethical issues remain, emphasizing the need for ongoing research to develop adaptable, expandable, ethical sentiment analysis models. The purpose of this review is to discuss the ground-breaking implications of advanced techniques for analyzing social media sentiment, along with future research directions.
Copyright (c) 2025 Fazal Tariq, Muhammad Tufail, Taj Rehman
Sustainable development of Vehicular Fog Computing Using Karlskrona Manifesto
The emerging concept of smart cities, smart vehicles and smart traffic systems using Vehicular Fog Computing (VFC) technology is based on the concept of cloud and edge computing which assist in the automation and implementation of such smart concepts. But implementing and utilizing Vehicular Fog Computing (VFC) will need an excessive amount of power for the communication and decision-making system and will also lead to cause a huge amount of electromagnetic radiation. The sustainable and environment friendly development and implementation of such systems and infrastructure is still a key challenge for the industry. This research article undergoes a thorough sustainability analysis of the proposed system using the Karlskrona manifesto, which helped us to identify the current and future viable goals and aim of the system and their order of impact on the individuals. The development of the Sustainability and Analysis diagram(SusAD) explored the immediate, intermediate, and structural impact of technical, individual, social, environmental and economic dimensions of the system, this research article also proposes alternative ways to achieve sustainability and prevent the adverse effects of the system implementation.
Unveiling Public Sentiments in Crisis: The EHDSAF Approach to Dark Social Media Data during the 2010 Pakistan Flood
The flood in Pakistan has become one of the severe natural disasters in the country that has killed millions of people and made a wide impact on the economy, social life, and environment of the region. However, while the focus was mostly on the ground response, human emotions, and sentiments, which were shared on social media, known as dark social media data, were left untapped. In this research, federated querying involving the proposed approach EHDSAF (Enhanced Hybrid Dark Social Analytical Framework) is employed to aggregate and analyze unstructured social media data on the 2010 flood disaster. Thus, using the proposed approach, we reveal the unconscious emotions of people in different regions and aged within Pakistan. The study shows the ability to consistently monitor the emotional status of the population, differences in positive, neutral, and negative attitudes depending on the region and age category. These findings suggest a more profound understanding of the process of communication of the experiences and reactions during the crisis and provide the information that may prove useful for strategizing the future disaster management and the carrying out of humanitarian interventions.
A Systematic Literature Review on Methodologies for Improving Software Quality in Software Development Process
For better repute in software monopoly, quality of product is one of the essential factors in any corporation. Quality of the product depends on the satisfaction of customer. Product quality is measured by customer satisfaction with that product. The goal of quality might be attained only over convenient fundamentals and policies. The enormous and developing nations are making improvements on every day for improving the quality of the products they manufactured to increase the satisfaction of their customers. Every corporation tries their best to design quality software that best suits to customer needs. For the purpose of better quality, some fundamentals and policies were made, however there might be distinct problems, there are several rationales for poor quality of product. We choose quantitative approach of research as methodology for our research. Basically, quantitative approach focuses on measuring and evaluating the data. This paper will address different issues that were corresponding to these rationales. Several techniques will also be explained, as well some alternative solutions are defined in this paper that will leads to good quality of any product or software. Furthermore, we conclude that systematic approach and incremental approach, these are good but the problem with these approaches is that requirements will be freeze. It will be complex and problematic to accommodate changes. In short, all techniques have their own impact on enhancement of software quality.
Copyright (c) 2024 Inam Ul Haq, Waseem Abbas, Mehran Mehran Shakoor, Haider
Design and Analysis of Ridge and Sub-Wavelength Grating Waveguide Based Micro Ring-Resonator Sensor
The subwavelength grating microring resonator based filter shows great promise in optical sensing and communication. However, it spectrum nature makes it unsuitable for tracking or extracting a single wavelength from a broadband light source. In this paper, we used side-mode suppression, based on an angular grating- subwavelength microring resonator which offer high flexibility in wavelength design within the silicon-on-insulator (SOI) waveguide. By periodically arranging silicon waveguide pieces in three different widths, we can set the effective index along the inner sidewall of subwavelength microring resonator. thereby can achieve the precise wavelength selection. We examine the effects of various key parameters on the center wavelength, side-mode suppressed ratio, and quality factor of this filter.The proposed structure's isalso analysed for coupling strength between the bus and subwavelength grating ring resonator. The wavelength selection, compact size, compatibility with other similar nature waveguide-based devices and design flexibility highlight its significant potential in compact optical sensing and optical communication
Copyright (c) 2024 Muhammad Favad Qadir, Muhammad Mahmood Ali
Numerical investigation of future potato yields in Bangladesh using Machine learning and IoT
The changing environment has significantly affected Bangladesh for the past several decades, and a number of minerals and nutrients are in short supply in the human population. However, not only are we being affected by the effects of climate change, but also the crops cultivated on our farms are feeling its impact. The nutrient content and growth of food crops are affected by many components of climate change, such as rising temperatures, increased precipitation, rising CO2 levels, etc. It is commonly regarded that potatoes are one of the healthiest and most popular foods in Bangladesh, with an abundance of nutrients and electrolytes that are beneficial to the body. However, in the not-too-distant future, the production of potatoes will be affected by the high temperature and decreased precipitation resulting from climate change. As potatoes are one of the most essential staple foods in Bangladesh, a viable solution must be found for continuous potato production in the country. A comprehensive examination of the evolution, production, and important nutrients of potatoes will be performed in this study. A range of sensors, networks, drones, cameras, and applications will be collected for the purpose of monitoring the potato field and ensuring that the crops are cultivated in an acceptable environment. This idea can aid our country economically and agriculturally by increasing potato production. Potatoes are affordable for consumers from all socioeconomic classes. Through this idea, Producers will achieve a successful harvest of nutrient-rich potatoes for public consumption.
Copyright (c) 2024 KABYO DEB, Rahul Das, SAURAV SARKAR, DR. MD. SOHIDUL ISLAM, NAZNIN NAHAR KHANOM MIM
Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation
The escalating incidence of road accidents poses a significant challenge to public safety, often exacerbated by delays in emergency response. This research introduces an IoT-based intelligent system designed to revolutionize accident management by providing real-time data and critical information to emergency services. By seamlessly integrating Raspberry Pi and Arduino microcontrollers with advanced sensors such as force-sensitive resistors (FSRs), GPS, and potentially additional sensors like accelerometers and gyroscopes, the system accurately detects accidents, assesses their severity, and captures comprehensive data, including passenger images, vehicle details, and precise accident location. Upon impact, the system rapidly transmits this data, along with pre and post-accident images, to a designated emergency response center via a secure and reliable communication network. This swift dissemination of critical information empowers emergency responders to make informed decisions, optimize resource allocation, and prioritize response efforts, ultimately saving lives and minimizing the impact of accidents. The system's intelligence lies in its ability to not only detect accidents but also to assess their severity based on sensor data analysis, enabling a more targeted and efficient emergency response. Furthermore, the system's potential to collect valuable data on accident patterns and causes can contribute to the development of preventive measures and road safety strategies. By leveraging the power of IoT technology, this research aims to significantly enhance road safety and transform accident management practices.
Copyright (c) 2024 Rahat Ullah Khattak, Abdullah
Design and Implementation of Switched Capacitor Banks Controlled by a Programmable Logic Controller for Power Factor Improvement of Three-Phase Induction Motors
Industries widely use three-phase inductive loads, such as induction motors, due to their cost-effectiveness, low maintenance, reliability, and durability. However, these loads decrease the power factor, which leads to power wastage, higher billing costs, and penalties from electric power supply companies. To address this issue, a system has been developed that employs a programmable logic controller (PLC) to enhance the power factor of three-phase loads, which reduces the plant's operating costs and lessens the demand for electricity supply from the utility side. The system combines hardware and software components. The software includes logic-based PLC programming that controls the sequence of operations step by step, whereas hardware consists of power and control circuits and protective devices. When an inductive load is added to the system, the PLC reads the input signal from the magnetic contactor's contactor coil connected with the motor and sends an output signal to the corresponding contactor coil of the magnetic contactor to switch the appropriate capacitor bank to enhance the power factor. This paper discusses the experimental results for seven cases utilizing different load combinations with and without the developed system. Thus, energy management requires improving the load's power factor, and the PLC is used as a power factor controller (PFC) for many industrial control applications.
Copyright (c) 2023 NUML International Journal of Engineering and Computing
Deep Communication: Exploring End-to-End Wireless with Convolutional Neural Network
In recent years, end-to-end wireless communication has gained significant attention in the field of wireless communication. In this paper, we propose a new approach to achieving end-to-end wireless communication using convolutional neural networks (CNNs) in the presence of Nakagami fading, Additive white Gaussian noise (AWGN), and multiple-input multiple-output (MIMO) fading channels. Further, we have applied the bursty noise to the AWGN channel. We first develop a CNN-based transmitter architecture that can efficiently encode information bits into signals, followed by a CNN-based receiver architecture that can accurately decode the received signals. Our proposed method leverages the strengths of CNNs in learning and extracting features from raw data and applies them to wireless communication. We then evaluate the performance of our proposed method by extensive sets of simulations in different AWGN, Nakagami fading, and MIMO fading channel scenarios. The simulation results show that the method proposed shows superior performance compared to existing state-of-the-art techniques at low Signal to Noise ratio(SNR) in terms of bit error rate (BER) and Binary cross-entropy loss. Our proposed method can be a promising solution for achieving end-to-end wireless communication in various practical applications.
Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques
This paper discusses the issue of primary user emulation attacks (PUEAs) in cognitive radio networks (CRNs). These attackers imitate the signal characteristics of primary users (PUs), preventing secondary users (SUs) from accessing their assigned spectrum. The research focuses on detecting PUEAs using the time difference of arrivals (TDOA) to detect the attacker and reduce detection errors using heuristic techniques. Differential evolution (DE) and cuckoo search (CS) algorithms are utilized to optimize detection accuracy. Simulation results confirm that the proposed CS algorithm outperforms and gives better performance as compared to the DE algorithm.
Copyright (c) 2024 Jabbar Mahmood, Rahim Ali Qamar, Shahzad Latif
Unlocking the Potential of IoT: Discovering the Next Big Advancements in Connectivity and Automation
The Internet of Things (IoT) has revolutionized the way we connect and interact with the world around us, bringing unprecedented levels of automation, convenience, and efficiency. As this transformative technology continues to evolve, researchers and industry experts are exploring emerging trends and technologies that have the potential to shape the future of the IoT landscape. In this article, we provide a brief overview of the current state of IoT and delve into key areas of innovation, including edge computing, artificial intelligence (AI), 5G connectivity, blockchain, and sustainable energy harvesting. These advancements hold immense promise in addressing challenges, enhancing security, enabling real-time applications, and fostering sustainability within the IoT ecosystem. Understanding and harnessing these emerging trends is crucial for unlocking the full potential of the IoT and driving its continued growth and impact on various industries and everyday life. Keywords: Internet of Things, IoT, edge computing, artificial intelligence, machine learning, 5G connectivity, blockchain, energy harvesting, sustainability.
Copyright (c) 2024 Mahrukh Tanveer, Muhammad Faizan Qureshi, Saif ullah Qamar
An IoT and Machine Learning-based Neonatal Sleep Stage Classification
Sleep, in neonates, is used to access the quality of brain and physical development. Typically, neonatal sleep has been divided into three stages: active sleep (AS), quiet sleep (QS), and intermediate sleep (IS). Polysomnography (PSG) is considered a gold standard to classify sleep. To address this issue, over the past two decades, researchers proposed multiple algorithms for automatic sleep stage classification. These algorithms work achieved outstanding results i.e. quiet sleep detection still, lacks in many aspects. One major drawback of the existing research is amalgamation of awake and active sleep into low voltage irregular (LVI) state. This amalgamation corrupts 40% of the overall EEG signal. For this reason, we proposed an algorithm for neonatal sleep-wake classification using machine learning. The proposed research is divided into three steps. Firstly, the EEG signal was pre-processed using finite impulse response filter to remove the noise and artifacts. Clean EEG signal is then divided into 4560 30-sec segments. Then, twenty prominent EEG features were extracted from time, frequency, and spatial domain. After feature extraction, support vector machine was used for sleep stage classification. The propounded study outperformed all the existing algorithms for sleep-wake classification with a mean accuracy of 83.7%. Four-fold cross-validation was used to validate the overall dataset. Multiple other performance matrices i.e. sensitivity, specificity, Kappa were calculated to prove the efficacy of the proposed study. Statistical results show that the proposed study can be used as a real-time neonatal sleep and Awake classification algorithm, as this did not use prior post-processing techniques.
Copyright (c) 2024 Awais Abbas, Hafiz Sheraz Sheikh, SaadUllah Farooq Abbasi
A Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation
The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.
Copyright (c) 2024 Amna Sajid, Muhammad Waqas Arshad
Solving Traffic congestion using Artificial Intelligence: A review
Artificial intelligence(AI) is the new electricity, that can assist any system present in the world. In this paper, we have discussed AI and its associated technologies currently available for traffic management. AI can be incorporated into traffic lights to make them smart and to meet new challenges. AI can also be used in combination with IOTs and in autonomous cars to make traffic flow easier, lower time wastage, decrease fuel consumption, and benefit the environment.
A Systematic Literature Review on SQL Injection Attacks
With the increasing use of web applications, concerns for data integrity and security have increased manifolds in the current time. The growth in quantity of internet clients and sites has made the web security circumstances progressively extreme. Structured Query Language Injection Attack (SQLIA) is a major threat to web applications. Over the time, many studies have explored the reasons and techniques of these attacks, and also ways to detect and prevent them from happening. This study presents a Systematic Literature Review (SLR) based on the methodology proposed by Kitchenham in 2007. The focus of study is on determining how and why SQLIA are done and how can they be avoided or mitigated. The literature is considered for a time period of four years; 2016 to 2023. Moreover, evaluation has been done, based on limitations and priorities proposed by each technique studied. Attack types with their severity has been reviewed that may help researchers propose new techniques in order to make web applications more secure against SQLIAs.
Copyright (c) 2024 Maryam Mehmood, Asad Ijaz
NLP-Based Analysis of Public Perception Regarding Covid-19 Vaccines
The Covid-19 pandemic affects millions of people throughout the world. Everyone's life gets disturbed because of this pandemic. The Covid-19 vaccines are now freely available, but people still get feared because of the rumors spread by social media. Despite recommendations from experts, people show conceptions and perceptions regarding vaccines on social media platforms. The main objective of this study is to introduce the methodology to analyze the public's views regarding covid-19 vaccines using a publicly available worldwide Twitter dataset. In this study, we have used Natural Language Processing to analyze the sentiments. For sentiment analysis, we use Keras embeddings (deep Neural network) and Top2vec for topic modeling. This research will aid the government so that they can distinguish the major issues and provide preventive measures by taking the help of social media.
Learning-Based Testing Using SAL (Symbolic Analysis Laboratory) Model Checker
This paper studies learning-based testing (LBT) for reactive systems with different learning algorithms and model checkers. LBT is a technique that requires a learning algorithm to learn the models to generate test cases automatically. We have used the generic methodology of LBT to test reactive systems with two different model inference algorithms (i.e., IKL, DKL) and two different model checking tools (i.e., NuSMV, SAL). To investigate the feasibility of LBT, we integrated our SUTs with these algorithms in LBT and tested if LBT optimizes test generation with these algorithms. We tested our SUTs with Boolean data types to check the difference in the working of model inference and model checking algorithms which we analyzed experimentally. The results show that LBT works better with DKL and SAL. DKL is a recently proposed model inference algorithm, and SAL is the latest model checker on which the research is being carried out. DKL and SAL algorithms explore errors in reactive SUTs with the h LBT framework more quickly and efficiently.