KIET Journal of Computing and Information Sciences
KIET Journal of Computing and Information Sciences (KJCIS) ISSN(P) 2616-9592 ISSN(E) 2710-5075 is the bi-annual, multi-disciplinary, HEC recognized 'Y' Category research journal published by the College of Computing & Information Sciences (CoCIS) at Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan. KJCIS aims to provide a panoramic view of the state-of-the-art development in the field of computing and information sciences at a global level. It provides a premier interdisciplinary platform for researchers, scientists, and practitioners from the field of computing and information sciences to share their findings and contribute to the knowledge domain at a global level. The journal also fills the gap between academician and industrial research communities.
Karachi Institute of Economics & Technology
Role of situational method engineering to help small and medium organizations by improving agile practices
Present computer systems are inclined to evolve under constantly growing conditions such as Applications. A growing and complicated project conditions required the use of a flexible approach for the development of complex projects. The agile technique's suitability depends on various situational factors such as team size, team experience etc. In third world countries, the selection of software methodology is a very difficult task. This is because most small and medium enterprises have very few resources and small size teams and for them to handle the variant situation is a very big challenge. Two major issues have been discussed in this study one is the identification of situational factors for situation specific projects that act as a hurdle and the second one is the designing of situational methods to handle the variant situation in agile for small and medium organizations. The reason for this research is to give a framework for constructing situation specific methods to handle the situations that vary from project to project, team to team to help the organizations that are of small and medium size. In formulating the framework, we perform a literature review to identify the challenges and situational factors that should be considered before the selection of a method for any project. Multi method research methodology used to formulate the framework. The prototype was developed to help the practitioner to understand our model. An experimental study helps to evaluate the developed framework. The result of the proposed framework helps to improve agile practices for small and medium organizations. In the end, results showed that it is a feasible framework which provides a solution for the variant solution by selecting situation specific methods with the help of using SME principles. Our results show by using our mythology companies getting better numbers. The numbers of mean and standard deviation increased by 7.40 from 4.22 and 0.49 from 0.29 respectively.
Copyright (c) 2026 KIET Journal of Computing and Information Sciences
Benchmarking SE-FPN Enhanced Deep CNN Backbones for Early Detection of Mango Leaf Diseases
Mango leaves are susceptible to different diseases which causes a huge impact on the quality and quantity of the fruit. This shows how crucial it is to have reliable and automated disease detection systems to help precision agriculture. In this research, we present a novel deep learning framework that combines a Squeeze-and-Excitation (SE) enhanced convolutional backbone with a Feature Pyramid Network (FPN) directed dense fusion classification head for the recognition of multi-class mango leaf diseases. The SE mechanism dynamically readjusts channel-wise feature responses to highlight distinguishing lesion traits, whereas the dense multi-branch fusion head consolidates high-level representations to enhance feature diversity and resilience. The model is trained in two steps: first, it warms up with a frozen backbone, and then it is fully fine-tuned to improve learning of features that are specific to the domain. A lot of tests done on a carefully chosen mango leaf dataset show that the proposed architecture works well with many backbone networks. For example, DenseNet121 and InceptionV3 have validation accuracies of up to 99.75% and low validation loss. A comparative analysis shows that the proposed SE–FPN framework is better than traditional CNN-based methods when it comes to accuracy, generalization, and stability. Furthermore, evaluations utilizing confusion matrices, ROC and precision-recall curves, and Grad-CAM visualizations validate the model's strong discriminative capacity and interpretability. Overall, the proposed strategy is a good, scalable, and easy-to-understand way to use precision farming to find intelligent mango leaf disease.
Performance Analysis of GoogleNet on Balanced and Imbalanced Image Classification Datasets
This study has revolutionized image classification by utilizing convolutional neural networks (CNNs). GoogleNet (Inception-v1) serves as a fundamental CNN framework because it incorporated the inception module to boost computational efficiency while maintaining high accuracy. This research explores the performance of GoogleNet on both balanced and unbalanced datasets using CIFAR-10 and CIFAR-100 as test cases. The research examines how well the model adapts while maintaining operational efficiency and classification performance under different testing scenarios. The study first evaluates how model performance is affected by class imbalance and then presents techniques to address this issue while comparing the results to other architecture model.The research investigates contemporary advancements in deep learning by analyzing hybrid models and attention mechanisms which integrate transfer learning techniques. The document evaluates the practical classification performance of GoogleNet while examining its strengths and weaknesses.
GoogleNet, CNN, Balanced Datasets, CIFAR-10
Optimized Violence Detection in Video Sequences Using Inception-ResNetV2 and Transfer Learning with Redundant Frame Reduction
Abstract Over recent years, violence detection has emerged as a crucial task in computer vision, driving advancements in innovative methodologies. This research employs transfer learning to identify violent scenes, remove redundant frames, and classify video frames as violent or non-violent. Using the Hockey Fight Dataset and Real-Life Violence Situation Dataset, the proposed framework integrates Inception v3, pre-trained on ImageNet, for feature extraction. A heuristic method reduces redundant frames, while Euclidean similarity enhances frame selection. The dataset is divided into 70% training and 30% testing using hold-out cross-validation. The model's performance is evaluated against five deep learning architectures: 3D-CNN, LSTM, I3D, ResNet50, and DenseNet121. Inception-ResNetV2 outperforms all models, achieving 96% accuracy on the Hockey Fight Dataset and 93% on the Real-Life Violence Dataset with Adam, and 97% and 91% with SGDM, respectively, with corresponding F1-scores of 97% and 91%. The 3D-CNN, LSTM, I3D, and DenseNet121 models show lower performance, with accuracies ranging from 64% to 91%. These findings underscore the superior capability of Inception-ResNetV2 for violence detection in video sequences.
A A Spider Web Optimisation Algorithm for improving Queue Tasks on highly loaded Nodes
A Lossy network is defined as a network which produced losing of data packet, time delay and network error during data transmission from start to end nodes. Due to highly loaded routers, switches, insufficient bandwidth and network utilization that effects reduction of throughput, request turnaround time and decreasing network reliability. This problem is mostly faced in a real time application such as video streaming, online gaming a voice over IP (VoIP). The best technique is to mitigate Lossy Network for adopting network congestion control algorithm, network coding and Quality of Service (QoS). A Swarm-based nature-inspired metaheuristic approach is a juncture of bionic science and Swarm intelligence. It is based on the collective behavior to solve complex distributed computational problems such as optimizing data traffic and minimizing queue tasks on certain thresholds—the two-stage bionic-inspired algorithm to forage food by eliminating pheromones to explore the positioning stage of cobweb. A cobweb is a hexagonal-based network model in a spider web that attracts other species through electric force to exchange information, reproduce, and generate offspring. A Cloud-based network model contains a variety of data, huge volumes of information expanding at different velocities. In this type of scenario, fog computing and a 6-G network are required for the survival of big data and network platforms. In spider webs prey to generate vibration to attract other spider species to move it where the rate of traffic is minimal therefore the queue task of certain nodes has been minimized to optimize the flow of traffic constantly everywhere. The newly SWO algorithm is a technique to solve complex engineering problem and it an attractive choice of newly practicians and researcher, a viable choice in the field of web optimization. Comparison of reduction of traffic via Circle multi-modal function 57.1% with Raster-gin function and Bohachevsky function are 42.35% and 52.9%.
Big Data, Cobweb, Pheromone, Spider, Swarm Intelligence
A Study of Q-Learning in the Taxi-v3 Environment: Reinforcement Learning for Optimal Navigation through Hyperparameter Optimization
Reinforcement Learning (RL) has widely showcased its effectiveness across a variety of domains including healthcare, robotics, gaming, and autonomous driving. RL involves teaching an agent to navigate through an environment whilst trying different actions to receive feedback in terms of rewards and penalties. This leads to an iterative process of learning how to take actions that provide the most rewards. A widely used model-free algorithm in RL is the tabular Q-learning algorithm which aims to identify an optimal policy by selecting actions that maximize rewards. This research takes a deeper look into the application of Q-learning in the Taxi-v3 environment, a popular environment for evaluating different RL algorithms. Specifically, our study focuses on hyperparameters and their optimization to determine how they impact the performance of the agent in the Taxi-v3 environment. To assess how the agent performs in the environment, we use the rewards that the agent obtains throughout each episode, the steps it takes in each episode to finish the task, and the loss values that indicate how well the agent was able to predict the optimal actions required for a given state. Our agent attained the highest reward during episode 1,196 during the initial values, and when we optimized the hyperparameters and used the fine-tuned values, the agent achieved the same reward during episode 248. This behavior of the agent after the fine-tuning process suggests that optimizing the hyperparameters leads to the agent learning an optimal policy early on in the training and improves the quality of the Q-learning algorithm in solving challenges that involve navigating through grid-based worlds.
Copyright (c) 2025 KIET Journal of Computing and Information Sciences
An ANN Based Intelligent System for Measuring Customer Loyalty
Today’s generation enjoys making purchases online. The online market is expanding quickly. The competition to sell items gets more intense as more stores enter this market. On the other side, these marketplaces are giving their clients convenient alternatives and earning their confidence. Customers are far smarter now; they research and weigh their alternatives before making a purchase. While some customers are frequent shoppers, others are still apprehensive about making purchases online. Because internet shopping has so many drawbacks, people are becoming increasingly aware of the need for a system that can properly analyze existing customers in order to attract new clients and business. One creative method used in this study to gauge consumer loyalty to a product is to enable new customers and news consumers make decisions more quickly. Our method uses a novel idea to assess a buyer's loyalty to a specific brand or product, and it might help a new consumer decide on a particular item based on its numerous features and previous customer reviews. In order to measure customer loyalty in our suggested model, we employed an artificial neural network (ANN) technique. To do this, we took a big data set from Kaggle that was based on customer evaluations of online products. The reviews' textual and non-textual information is extracted via POS tagging, which then pre-processes and turns the textual data into tokens. The suggested ANN method uses pre-processed and mapped reviews to create vectors. The proposed ANN model receives the highlighted dataset for training. The proposed method is tested using additional sample data after training. The trained dataset is utilized to predict loyalty. In along with adding to the body of knowledge on predicting consumer loyalty, this study is expected to offer e-commerce managers insights for pursuing customer loyalty.
Artificial neural network, customers loyalty, Data Analysis, NLP technologies, features selection, amazon.com
A Malicious JavaScript Code Detection using Deep Learning Approach on Imbalanced Dataset
A web application refers to a software or computer program that makes use of a web browser. In the present day, the categorization of JavaScript-based attacks has significant importance, primarily driven by the substantial increase in the number of Internet users. JavaScript is a crucial component in the field of web development, serving as a major instrument for hackers to initiate attacks. When the presence of malicious JavaScript is identified within Web sites, the rate of detection is seen to rise while the rates of false-negative and false-positive outcomes are observed to decrease. Most of the existing work validates their approaches using balanced datasets which can not be generalized to real-world scenarios. In this paper, we employed GPT-2 as data augmentation technique to address the class imbalance problem. The resultant balanced dataset is then utilized as input for the DOC2Vec algorithm, which facilitates the process of vectorization. The resulting features are then fed into an attention-based Bi-LSTM model. The results demonstrate that the proposed model achieves superior performance in terms of recall and f1-score, with values of 0.74% and 0.50% respectively
AI-Driven Multimodal Summarization: Generating Coherent Textual Summaries from Video Content using NLP Techniques
The process of deriving a summary from a given sequence of sentences is known as text summarization. There are two different kinds of summaries: extractive and abstractive. In an extractive summary, words are taken out of the original text and combined into a brief. In addition to reproducing the words from the input, the abstractive summary also creates new terms based on its comprehension of the text. This report explores the development and implementation of a system aimed at generating textual summaries of videos solely from audio content. The system utilizes cutting-edge approaches in Natural Language Processing (NLP) and Machine learning (ML), Language Models (LM). It employs Whisper model and BART model to transcribe spoken audio, extract meaningful information, and summarize the content to create concise video summaries. By combining these models and techniques, this system is capable to handle both English, Hindi/Urdu and the bilingual conversational videos and is generating the correct results with an average accuracy of 70% (ROGUE Score) and 100% of F1-Score (ROGUE).
A Enhanced Intrusion Detection Using CNN-LSTM with Advanced Evaluation Metrics
At present, network attacks have become a worldwide issue as they disturb the functioning and performance of the computer network. Network attacks are a serious problem they may cause the loss of important information, hacked personal data, and threats for demands money. Intruders today use more advanced type methods for hacking personal data and information as they break the traditional techniques used in the prevention of network intrusion. A more powerful and successful method is required for prediction. The deep learning method is suitable for this problem which is powerful and efficient in prediction. One of the famous types of deep learning models is the convolutional neural network model and another Long Short Term Memory. In this paper, the convolution neural network model combined with LSTM is proposed for the prediction of network intrusion which is one-dimensional. The proposed model is multiclass and tuned by different parameters to obtain the best efficiency from the model in the case of the multiclass dataset. This multiclass model is trained on the two multiclass datasets to get the best accuracy from the model on datasets. The first dataset named as wireless network dataset which contains four or five types of intrusion. The second dataset is the Microsoft Malware dataset that contains the eight or nine-class intrusion type. The experiment from the proposed model gives 0.996%, and 0.985% accuracy performance in multiclass prediction of network intrusion. The performance of the proposed hybrid CNN-LSTM model shows better performance than existing approaches.
Network Intrusion, Long Short-Term Memory, Convolution Neural Network, Deep Learning, etc
Topic Modeling and Identification in a Resource-Scarce Roman Urdu Language
In an age dominated by the Internet revolution, companies are making their businesses available to diverse groups of customers by leveraging the usage of e-commerce. To keep track of customer satisfaction and a competitive edge in the market, e-commerce businesses need to scrutinize their customer reviews. The manual approach to analyzing customer reviews is time and effort-consuming. Automated product review analysis exists but resource-poor languages like Roman Urdu lack such resources. To overcome this problem, this research presents a solution by incorporating Topic Modeling for Roman Urdu product reviews. A dataset of 8K Roman Urdu product reviews was curated from an online shopping platform. Various language-specific data cleaning steps were applied to data in the pre-processing step before experimentation. Different algorithms for Topic Modeling were implemented, out of them BERTopic produced outstanding results leaving the others behind. The results were also evaluated with an open-source dataset to check model generalization and reliability. Utilizing the power of machine learning and recent approaches, this study is a step forward to automated review analysis in the Roman Urdu language.
Natural language processing, Topic modeling, Low resource language processing, Roman Urdu Language, BERT Model.
Social Distance Assessment and prevention system Based on Marker
IoT is quickly becoming a leading technology in healthcare. Early detection of health problems and preset protocols after patient recovery are all employed to decrease the chance of COVID-19 spreading to others in the event of COVID-19. Such wireless positioning devices can correctly remind individuals to maintain distance by sensing between people and then warning them if the people are close to each other. Motivated by this notion, in this paper we have proposed and implemented a model of the social distance assessment, monitoring, and marker system for prevention. The goal is to minimize the effects of the coronavirus outbreak while generating the least amount of economic harm possible, as well as to enable or even impose social distance. In the Monitoring System, users can easily access a web-based application that is integrated with the detection system by following the integration with the Raspberry Pi 4 and Pi Camera, in which they can monitor the detection of safe and unsafe people. Meanwhile, the marker system that is based on a laser will guide the user to stand in safer locations with the help of a laser marker module to eliminate violations. The proposed system is implemented using OPEN CV and Mobile NET SSD for object detection and uses the Euclidean Distance measurement method for measuring the distance between people. The hardware and software integration is also included in the system with an accuracy level, the system is an effective, low-cost, and user-friendly social spacing tool for preserving distance around people at a large gatherings.
A Prediction of Network Intrusion Using CNN-LSTM
At present, network attacks have become a worldwide issue as they disturb the functioning and performance of the computer network. Network attacks are a serious problem they may cause the loss of important information, hacked personal data, and threats for demands money. Intruders today use more advanced type methods for hacking personal data and information as they break the traditional techniques used in the prevention of network intrusion. A more powerful and successful method is required for prediction. The deep learning method is suitable for this problem which is powerful and efficient in prediction. One of the famous types of deep learning models is the convolutional neural network model and another Long Short Term Memory. In this paper, the convolution neural network model combined with LSTM is proposed for the prediction of network intrusion which is one-dimensional. The proposed model is multiclass and tuned by different parameters to obtain the best efficiency from the model in the case of the multiclass dataset. This multiclass model is trained on the two multiclass datasets to get the best accuracy from the model on datasets. The first dataset named as wireless network dataset which contains four or five types of intrusion. The second dataset is the Microsoft Malware dataset that contains the eight or nine-class intrusion type. The experiment from the proposed model gives 0.990%, and 0.985% accuracy performance in multiclass prediction of network intrusion. The performance of the proposed hybrid CNN-LSTM model shows better performance than existing approaches
Enhancing Histopathological Image Classification: Optimal Fine-Tuning of Convolutional Neural Networks with Feature Extraction
The area of medical image analysis has genuine obstacles, such as small data sets, complicated fine-tuning, and choosing the right architecture. This paper proposes a CancerVisionNet model based on the convolutional neural network (CNN) architecture with many layers to extract and classify features from cancer images. To train the CancerVisionNet model and avoid overfitting, data augmentation is carried out using a data set consisting of 220,025 images. The proposed CancerVisionNet model is evaluated on the PatchCamelyon dataset. Its remarkable area under the receiver operating characteristic (ROC) curve (AUC) measure is about 0.9. Compared to the other studies, the CancerVisionNet model stands out with a higher accuracy (95.4%). Moreover, this work demonstrates the potential of CNNs in medical image analysis, providing an effective approach to enhance classification accuracy and paving the way for further advances in the field. Although the results of this study pertain to histopathology and the PatchCamelyon dataset, the potential for a broader application awaits cross-domain validation. Future research works can include exploring alternative architectures and scalability to larger datasets.
Rice Varieties (LULC) Classification using Artificial Neural Network through Landsat 8 OLI Image
Multi-temporal and multi-spectral remote sensing images are good instruments that may help traditional agricultural systems by properly monitoring and calculating crop yields prior to harvesting. Traditional agricultural systems mostly depend on limited ground-survey data. Time-series satellite images of vegetation phenology serve a significant role in vegetation monitoring and land-cover categorization because they can record vegetation information at various development phases. It's impossible to overstate how important remote sensing has become in the last several decades. In order to monitor land surface dynamics, natural resource management, and the general status of the ecosystem, remote sensing sensors have been used from space. In order to begin vegetation conservation and restoration projects, it is necessary to know the existing status of plant cover. Several early studies of ecosystems and land cover have used agricultural census data to map paddy rice fields at the global and regional scales.
Land Use / Land Cover, Crop Classification, Remote Sensing
A Hybrid Model for Human Behavior Recognition Using Emotions, Sentiments, and Mood Features
While social networking is a powerful communication tool, the obscured behavior of individuals on social networks remains a significant problem for users. Currently, research work is being focused on formulating mechanisms to determine the obscured behavior of users for secure and trustworthy social media. The proposed model employs mathematical formulation and multinomial classification of mood and emotions to analyze the conduct of an individual, thus enabling social trust on social media. First, natural language processing techniques are applied to predict the emotions, moods, and sentiments of an individual from the text, and then a mathematical model is applied to gather a comprehensive picture of one’s behavior using calculations at numerous instants. Finally, a subsequent trust state log is built in terms of positive and negative states which show the devotion in behavior in terms of mood, sentiments, and more significantly emotions. The efficiency of the proposed work has been demonstrated using simulation-based and real-world datasets along with individual behavior graphs for various conversations
Social Networks, Social Trust, Positive and Negative state log, Mathematical framework, Hybrid tactic, Multinomial classification, Human behavior, Natural Language Processing
Copyright (c) 2024 KIET Journal of Computing and Information Sciences
Predicting and Characterizing piRNAs and their functions Using an Integrated Machine Learning Approach
One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%. One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%. One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%.
Predicting Student Performance Using Educational Data Mining: A Review
Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified
Accurate Attack Detection in Intrusion Detection System for cyber threat intelligence feeds using machine learning techniques.
With the advancement of modern technology, cyber-attacks are continuously rising. Malicious behavior in the network is discovered using security devices like intrusion detection systems (IDS), firewalls, and antimalware systems. To defend organizations, procedures for detecting threats more correctly and precisely must be defined. The proposed study investigates the significance of cyber-threat intelligence (CTI) feeds in accurate IDS detection. The NSL-KDD and CSE-CICIDS-2018 datasets were analyzed in this study. This research makes use of normalization, transformation, and feature selection algorithms. Machine learning (ML) techniques were employed to determine if the traffic was normal or an attack. With the proposed study the ability to identify network attacks has improved using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.
Cyber-threat intelligence (CTI), Security Information and event management (SIEM), Intrusion detection system (IDS), Intrusion prevention system (IPS), Denial of service (DoS), Principal component analysis (PCA), Support vector machine (SVM), Indicators of compromise (IoCs),
Power Optimized Task Scheduling using Genetic Algorithm (POTS-GA) in Cloud Environment
In a cloud environment, the allocation of tasks has become pivotal on account of rapid growth of user requests. The processing of user requests leads to a significant execution time, and a huge amount of power is also consumed. Consequently, task scheduling for optimizing makes pan and power usage has become critical, particularly in a heterogeneous environment. This research work proposes Power-Optimized Task Scheduling using Genetic Algorithm (POTS-GA) that aims to minimize execution time and power consumption. The proposed strategy employs genetic algorithm to take scheduling decision while taking into consideration the execution time and overall power consumption of resources. The fitness computation considering both objectives and the customized genetic operators ensure to search for a better scheduling solution. The experiments performed on a large number of tasks and virtual machines show that the proposed POTS-GA approach outperforms other task scheduling strategies including Efficient Task Allocation using Genetic Algorithm (ETA-GA), Round Robin algorithm (RRA), and First Come First Serve (FCFS) and Greedy algorithm in terms of makes pan and power consumption.
Impediment in Adaptation of Algorithm Trading: A Case of Frontier Stock Exchange
The global financial markets have been significantly affected by the rapid change in technology. The study is an attempt to get to know the barriers to not adopting algorithmic trading in conventional stock exchanges. This research aims to plan and analytically proposed a model for explaining the reasons why frontier stock exchange traders and investors are hesitant to adopt algorithmic trading as a tool. The research includes variables; Lack of awareness, Trust, Lack of Government interest, unemployment, and unnecessary investment, which were extracted from previously available literature based on the theory of reason and technology acceptance model (TAM). A sample of 50 traders/investors from Pakistan stock markets was taken by using convenience sampling. Data was collected through a questionnaire and analyzed using correlation and linear regression techniques. The results show trust factor is the biggest hurdle in implementing Algorithm Trading which means countries like Pakistan which are following conventional methods for trading in stock markets have great doubts about the efficiency of Algorithm base trading because of the less human interaction and dependency on machines. Fear of miscalculation and the inexperience of data engineers are also one of the reasons conventional stock exchanges are reluctant to adopt algorithm trading. Similarly, variables like Lack of Government interest, unnecessary investment, and employment have a significant effect on the implementation of algorithm trading. Moreover, lack of awareness is the least significant factor, which shows the traders and investors in the Pakistan Stock Exchange are well aware of algorithm trading but the results cannot be generalized to the population due to a limited sample size of the study.
Copyright (c) 2023 KIET Journal of Computing and Information Sciences
SD-ALB: Software Defined Adaptive load balancing in Data Center Network
Network monitoring has crucial importance in data center networks to analyze the behavior of the underlying network. This analysis is used for working on multiple network parameters and load balancing is one of them. This article proposes an adaptive load-balancing approach to balance the load between the servers while changing its behavior with a change in traffic. Software Defined Networking (SDN) provides the single point of network configuration called SDN controller. This approach is facilitating the easy implementation of adaptive load balancing in Data Center Networks. The proposed approach is an extension to the LBBCLT load balancing approach that uses a dynamic probe generator to probe the servers about the response time and link bandwidth. We incorporate a path selection module in it and the path is selected using the Ant Colony Optimization. The results show that the bandwidth consumption and throughput have been improved and servers are receiving the load according to their capacities.
Guidelines for the Development of Automated Test Case Designing and Generation Tool(s)
Test case generation is the most intellectually demanding and labor-intensive activity in software testing. It plays the most important role in the quality of software products and reduces testing costs. Among all these automated test case generation is the most emerging and challenging area in today’s research. Currently, several test case designing/generation tools exist but they are disorganized and unstructured since they are implementing various sets of parameters which is hard to categorize that’s why they are still misaligned with the current requirement of the software industry. There exists a real need to have such a Test Case generation framework that aligns functions of Automated Test Case Generation tools with the current requirements of the software industry. Thus aim of this research is to provide guidelines for the development of automated test case designing/generation tools. To do so we have performed a literature survey which identified six common referred tools and existing proposed frameworks. After that, an international questionnaire-based survey had been conducted in software industries to identify their challenges related to test case generation tools and desired parameters. The results indicated that industries are not satisfied with the current tools and need more sophisticated tools. After analysis, it is concluded that software test case generation is a long process that starts from the identification of parameter sets and ends with the test procedures. It is evident from the results that all assembling test sets activities are highly supported (by all variables) by large and medium scale organizations, highly experienced quality engineers, and organizations that are certified by CMMI (at levels 4 and 5) and ISO(9001 and 90003).
Deep Learning based Market Basket Analysis using Association Rules
Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.
Market Basket Analysis, Association rules, Deep Learning, Frequent Pattern Mining, Retail Datasets
Nice PC Maker: An Online Interface to build Custom PCs
A custom-built PC is assembled to cater a specific user needs. Internationally, there are many e-stores available for users which offer them to build and order their systems as per their needs. In, 2020, Pakistan observed a covid-19 lockdown and mostly international shipments are cancelled due to the pandemic. This real world situation highlighted the deficiency of local market to fulfill the demands of custom-built PC users. In this paper, we propose and design a generic architecture of an e-store (Nice PC Maker (NPM)) to facilitate users with custom-build PC option along with other added features such as pre-build system purchasing and PC components purchasing. WooCommerce product builders (wordpress) are used to implement the architectural design. We also configured this generic architecture for a local client which helpes them to increase their business and facilitates users to get their desired systems.
Copyright (c) 2022 KIET Journal of Computing and Information Sciences
Counterfeit Recognition of Pakistani Currency
Forgery of currency is one of the major problems in money transactions all over the world. This is intended to exacerbate corruption and hindered the development and growth of the economy. As the resources for development are increasing in the present times, similarly, the sources of fraud are also increasing. Thus, making counterfeit notes have become even easier with the use of modern technology. Detecting and recognizing fake notes physically become a tedious and muddled practice hence there is a requirement for automatic techniques which perform the desired task more effectively. Therefore, simple solutions are proposed for a novel application. The proposed methods are based on the automated environment framed in MATLAB which reduces human efforts and the probability of error as a result, the efficiency of identifying the falsification in the Pakistani currency is optimized. In these methods, the generalized parameters are used for identification which is recommended by the governed financial body of Pakistan. This work is achieved through image processing techniques that perform acquisition and characteristic extraction, followed by some straightforward algorithms that perform verification checks on banknotes to authenticate serial numbers, security threads, and picture watermarks. The simplicity of these algorithms allows for the processing to be done swiftly and efficiently. The idea of the paper is to develop a system that can not only recognize Pakistani notes but is also capable of detecting fake notes, separating the fake notes from the stack of money into its compartments, and displaying the number of fake notes.
Automation, Counterfeit, Currency, Detection, Fake notes, Image Processing, MATLAB, Recognition, Sorting, and Template Matching.
Temporal Distance Issues and their Mitigation Strategies in GSD: An Empirical Study
GSD has several advantages that make it easier for software companies to follow this trend, like skillful workers, lower costs, and an improved marketplace. To achieve the benefits mentioned above, distributed teams must communicate effectively. Communication is a major problem for development, especially in GSD. Communication issues are classified based on temporal, geographical, and sociocultural distances. The temporal distance problem affects more GSD operations than the other hazards combined. Organizations can mitigate these obstacles and attain the desired quality by employing mitigation strategies while adhering to budget and time constraints. The authors of this study conducted a systematic literature review to identify temporal distance issues and their respective mitigation strategies to develop a conceptual framework. Additionally, a questionnaire interview with closed-ended questions was conducted. Without an empirical study, it is difficult to determine the precise relationship between temporal distance issues and mitigation strategies. The primary objectives of this study are to experimentally evaluate the proposed conceptual framework and the effects of selected risk mitigation measures. This study identified forty-four mitigation techniques and nine temporal distance challenges with a focus on a GSD-based environment, empirical research was conducted using data collected from Pakistani software companies. The findings of this study indicate, with a p-value of 0.01, that various mitigation techniques significantly impact various temporal distance issues. Based on the findings, software organizations can employ appropriate mitigation techniques to address temporal distance issues and reduce the failure rate of software projects.
“Global software development”, “temporal distance issue”, “communication issue”, “Empirical study”, “mitigation strategies”, “mitigation practices”, distributed software development, WarpPLS
Stock Prediction for ARGAAM Companies Dataset
Economic forecasting provides excellent profit opportunities and is a major motivator for most researchers in this field. In the fast-growing business world, the behavior of stock prediction is challenging for most stockholders and commercial investors. It provides benefits to investors to invest more confidently. Machine learning is an emerging technology that provides the capability to learn on its own through real-world intercommunications. Regression is the fundamental technique in machine learning which is useful for real-time applications. This paper experiments with stock price prediction effectively by using three machine learning techniques i.e. linear regression, decision tree, and support vector machine. The techniques were applied to the ARAMCO and Saudi Dairy dataset and the performance is evaluated using various parameters such as R2 value, MAPE, and RMSE. The results substantiated the hypothesis.
Evaluation of Image Support Resolution Deep Learning Technique based on PSNR Value
Machine learning is a subset of deep learning. Deep learning techniques have become increasingly popular in recent years for a variety of applications. Many techniques are used in image processing for image enhancement, object detection, and a variety of other purposes. Many deep learning techniques are important in single-image super-resolution (SISR). Many models are used for SISR, and they produce good perceptual and adversarial results. The procedure involves creating a high-resolution (HR) image from a low-resolution (LR) image, including some popular SISR methods: "Super-Resolution with Convolutional Neural Network" (SRCNN), "Super-Resolution Residual Network" (ResNet), "Super-Resolution Generative Adversarial Network" (SRGAN), and "Enhanced Super-Resolution Generative Adversarial Network" (ESRGAN). These deep learning models are layered, with each layer performing a different task for an LR image. These methods convert a single LR image into an HR image. A single LR image yields many high-resolution images. This paper creates a dataset of DSLR images for use with the SISR method. Deep learning technology is used to convert an LR image to an HR image. The HR images are then compared to the DSLR images. The "peak signal-to-noise ratio" (PSNR) and the "Structural Similarity Index" (SSI) are used to compare them (SSIM). When compared to the old method, the proposed method produces identical results. SSIM and PSNR are shown for both old and proposed approaches. Both approaches possess almost comparable results. The higher the PSNR value, the better the image quality. The same is the case with the SSIM value
Image Support, PNSR Value, SISR, SRCNN, ResNet, SRGAN, ESRGAN, SSI
An efficient Convolutional Neural Network (CNN) Based Image Colorization Technique
Color in any image playing a vital role for better understanding and real image visualization. In past when there was no concept of colored images each black and white image need to highlight its own merits to explain an image in a better way, but when the concept of colored images started the things were totally changed to describe and highlight key features and attributes of an image in a more better and effective manner. The key issue is that the old images and videos are color less and in some cases it needs to be colorized. There are a number of applications are uses to colorized a gray-scale image. The authors of this paper also presented an efficient deep learning based image colorization technique. The proposed system maps all the gray contrast pixels of the image into its corresponding colored pixels to produce a colored image using Convolutional Neural Network (CNN) technique. The proposed system is totally an automated system and it avoid any manual work or user hand-define rules. The system first read the gray contrast of each pixel of image; matches function then create the corresponding color contrast from the color map and finally convert the gray contrast image into color image. The system tested around 100 images and found accuracy about 95%.
Image, colorization, convolutional neural network, gray-scale, features