bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Energy Efficiency and Coverage in HetNets through Antenna Optimization
Authors: Dahri Safia , Shaikh Muhammad Mujtaba , Panhwer Fozia , Qureshi Fatima , Pali Iqrar ali , Afzal Muhammad
Published: January 2024

The advent of 5G technology has opened up new opportunities across various fields, including healthcare. In the context of wireless communication, the deployment of a two-tier heterogeneous network (HetNet) plays a crucial role in ensuring robust connections among devices, users, and healthcare infrastructure. This study focuses on optimizing coverage and energy efficiency (EE) in HetNets, tailored for specific application domains. We begin by examining how antenna height impacts coverage within macro and pico cells. Precise antenna placement is critical, as it significantly alters coverage patterns, particularly in healthcare settings, which can range from large hospital complexes to remote telemedicine locations. Additionally, we investigate the strategic adjustment of antenna gain in macro and pico cells, showing how this optimization enhances coverage and minimizes interference. Achieving this balance is essential for the reliable transmission of data. Our research also considers the interplay of antenna height, EE, and the maximum number of users ($N_{max}$). Surprisingly, we find that $N_{max}$ has a limited impact on coverage and EE compared to antenna configuration. This emphasizes the crucial role of antenna design and placement in crafting efficient wireless networks.Our study provides insights into improving coverage and EE in HetNets, with implications for various applications. It underscores the importance of strategic antenna optimization in shaping efficient and resilient communication systems. Furthermore, we explore the impact of $\beta$ on EE and leverage idle mode capabilities for further EE enhancements.
Author Profiling from Short Romanized Urdu Messages: A Preliminary Investigation using Transfer Learning Models
Authors: Ali Abid , Muhammad Sohail Khan  , Khan Muhammad Amin
Published: January 2024

Author profiling, a crucial task in natural language processing, involves identifying various attributes of an author, such as gender and age, from text. This study examines how transfer learning models in the context of author profiling from Roman Urdu text. We conduct experiments employing prominent models such as ELECTRA , BERT, RoBERTa, XLNet, Distil Bert, Distil RoBERTa,. Our analysis reveals superior performance in gender prediction using BERT, attaining an accuracy of 0.74698, precision of 0.7505, recall of 0.7462, and F1 score of 0.7456. On the other hand, RoBERTa demonstrates remarkable proficiency in age prediction with an accuracy of 0.8221, precision of 0.8215, recall of 0.8221, and F1 score of 0.8215. These findings showcase the effectiveness of transfer learning models in author profiling tasks offer insightful analysis for further research and applications in this domain.
Comparative Analysis of Feature Extraction Methods for Cotton Leaf Diseases Detection
Authors: Mehmood Shahzad , Memon Farida , Nighat Arbab , Memon Fayaz Ahmed , Saba Erum
Published: January 2024

Cotton leaf diseases must be accurately detected and classified to reduce plant diseases and output losses. Feature extraction strategies for automated cotton leaf disease diagnosis are compared in this study. The research uses HOG, SIFT, SURF, GLCM, and Gabor wavelets filter feature extractor to extract features. We gathered and preprocessed 2400 cotton leaf images of healthy and diseases, Angular Leaf Spot, Bacterial Blight, Cotton curl leaf disease (CLCuD), as well as Alternaria Disease. K-means clustering separates leaf areas and improves feature extraction in image segmentation. Discriminative features are extracted using the mentioned methods, and Support Vector Machine (SVM) classifier is employed for disease identification. The comparative analysis based on Accuracy, Precision, and Sensitivity reveals the Gabor Wavelet Filter Feature Extractor as the top performer, achieving 92% accuracy on the test dataset containing bacterial blight, curl virus, alternaria, and healthy leaves. While HOG, SIFT, SURF, and GLCM methods also perform well, they are outperformed by the Gabor Wavelet method. This study shows Gabor wavelet-based features can accurately identify and classify cotton leaf illnesses, helping farmers fight plant diseases. The results underscore the need of choosing proper feature extraction methods for autonomous plant disease diagnostic systems.
Privacy-Preserving Based Technique for Customer Churn Prediction in Telecom Industry
Authors: Khan Gul Zaman , Ikram Ulhaq  , Ihsan Adil  , Sajad Ulhaq  , Inam Ullah
Published: January 2024

In recent years, customer churn has been one of the most prominent topics, especially in the telecom industry. The telecommunications industry is producing massive amounts of data every minute. Thus, the telecom industry is seeking more ways to analyze and predict their potential and churn customers. According to telecom analysis, acquiring a new customer is costlier than keeping a current one. To lessen customer churn, it is very compulsory for industries to detect an increase in customer churn factors. The number of service suppliers is increasing daily, especially in the telecom industry. Phishing attacks and fraud are crucial points in customer churn. The aim of this study is to predict customer churn with profitable churn models for retention campaigns to satisfy the business requirement of profit maximization. The proposed research used the BAT-ANN classification model with the BigML dataset to predict customer churn in the telecom industry. The proposed model achieved 89.2% testing accuracy.
User-Centric Advertisement using Software Sensors Technique
Authors: Baloch Abdul Rehman , Kamran Taj Pathan  , Prof. Dr. Azhar Ali Shah
Published: January 2024

Contextual advertising is one of the most critical components in the economic system of internet due to increase internet publisher’s income highly dependent on the user-centric advertisement that is displayed on the sites according to the user context during interaction with the multiple sites. Previous contextual advertisement research work generally emphasises on investigating either to the keyword they type, content of the sites or uses any other application from the network hence, this finding has identified work when being extended through the user’s context. In this work we have looked at users’ profile information and user preferences to reach the users according to their context. These smart devices are ready with all capabilities to give useful contexts including information about physical environment, social connection, user internal and external context. These logical contexts beyond just content of the web pages, search keywords, and profile information are well used and organized for user-centric advertising. Here we are also arguing the appearances of the logical contexts which are available on the user browser, profile and visibly define the challenges of results from these logical contexts to improve the advertisement. We present a user-centric advertisement architecture and model that collects to integrate the users’ profile context and activity context to select, generate and to present advertisement with context. Finally, we discuss to implement the aspects of design and one specific application and outline our plans for future.
Clusters of Success: Unpacking Academic Trends with K-Means Clustering in Education
Authors: Agha Dua , Meghji Areej Fatemah , Bhatti Sania
Published: January 2024

Integrating Educational Data Mining (EDM) into the sector of education has heralded a new era, profoundly impacting learning outcomes by analyzing student performance and preventing academic disengagement. Using the K-Means clustering approach, this study carefully examines the academic accomplishments of students at Mehran University of Engineering and Technology and offers a sophisticated view of student performance patterns through the rigorous analysis of their learning data. A dataset comprising of the academic data of three student batches of the Department of Software Engineering was broken down into the subject categories of Computer Core, General, and Mathematics. The approach of clustering was then applied to find distinct performance patterns across the three subject categories. The findings of the research reveal that students have the highest performance in the computer core category, followed by mathematics, while the weakest overall performance across all three batches was exhibited in the general subject category. The study highlights the disparities in academic performance across distinct clusters and adds to our understanding of academic success while also illuminating the complex interactions between student characteristics and educational outcomes, providing useful information for educators and policymakers.
An Ensemble Modeling Approach to Enhance Grade Prediction in Academic Engineering Programming Courses
Authors: Mahboob Khalid , Sarfaraz Abdul Sattar Natha  , Syed Saood Zia  , Priha Bhatti  , Abeer Javed Syed  , Samra Mehmood
Published: January 2024

Predicting the future academic grades of students can play a pivotal role in enhancing their performance in specific courses, consequently yielding a positive impact on their prospective academic, professional, and personal achievements, as well as on society at large. The field of programming is rapidly gaining prominence as an essential profession spanning multiple domains, marked by abundant opportunities and financial rewards. To cater to the diverse interests of students, the recommended curriculum structure for engineering programs in computing adeptly combines theoretical knowledge with practical programming skills. This approach ensures that students acquire a comprehensive understanding of programming courses, allowing them to choose the path that aligns best with their envisioned careers as programmers This research endeavors to introduce ensemble prediction techniques aimed at identifying students who exhibit the potential for advancement, or conversely, those who may not excel in four university-level programming courses. The outcomes of this study are presented alongside valuable performance assessment metrics for five ensemble methodologies, namely AdaBoost, Bagging, Random Forest, Stacking, and Voting. This evaluation employs a 10-fold cross-validation methodology and incorporates the Principal Component Analysis (PCA) for feature ranking. The results unequivocally demonstrate that both the Stacking and Random Forest ensemble approaches have attained the highest level of accuracy when applied to two distinct datasets.
An Efficient Deep Learning Approach for Prediction of Student Performance Using Neural Network
Authors: Namraiza  , Abid Kamran , Aslam Naeem , Fuzail Muhammad , Maqbool Muhammad Sajid , Kainat Sajid
Published: January 2024

In recent years, schools have shown interest in utilizing data mining to improve the quality of education. To enhance academic performance, accurately predicting how students will perform in their classes is crucial, which is essential for their progress in further education. Some students encounter challenges upon entering higher education, and predicting their performance early on is vital to keeping them on the right track. Our research aims to assess student performance using various classification strategies to identify the most accurate one. We utilize a Kaggle dataset for this study. Initially, we clean up the dataset by removing duplicate records and filling in any missing information. Subsequently, we apply six different classifiers, including Neural Networks and methods such as Random Forest and Support Vector Machine, utilizing the Weka tool. Additionally, we employ Principal Component Analysis (PCA) to extract optimized features that enhance model accuracy. We evaluate all models on Training and Testing splits, as well as the 10-K Fold options provided by the Weka tool. Finally, we calculate Training Accuracy, Testing Accuracy, Precision, Recall, and F1-Score for each model and compare their results. Notably, Neural Networks and Random Forest demonstrate superior results compared to other models.
Securing Electronic Health Records using Blockchain
Authors: Agha Dureshawar
Published: January 2024

This research explores the application of blockchain technology in securing Electronic Health Records (EHRs) while integrating IoT sensors for real-time patient monitoring. The primary goal is to address critical healthcare industry challenges, including security, privacy, data integrity, and accessibility. Our system focuses on enhancing EHR security and reliability through blockchain's decentralized and tamper-resistant features. Additionally, IoT sensors provide real-time monitoring of vital signs, enabling prompt interventions. This study not only delves into technical aspects but also considers practical implementation in healthcare, contributing to improved data security and patient care.
Enhanced Diabetic Prediction Using Fuzzy C-Means Preprocessing and Random Forest Ensemble Learning
Authors: Bhatti Priha , Mahboob Khalid , Naeem Syed Saad , Bhatti Iqra Heer , Kamran Noorulain
Published: January 2024

Diabetes claims the lives of thousands each year, and many individuals remain oblivious to their condition until it reaches a critical stage. This study presents a data mining-based approach aimed at enhancing the early detection and prediction of diabetes, utilizing data from the Pima Indian Diabetes dataset. Despite the adaptability of fuzzy C-Means for various data types, the ultimate outcome of the clustering process hinges on the initial placement of cluster centers. Additionally, precision in data clustering is crucial; it can furnish either extensive, well-grouped data for the random forest or limited data, constraining its efficacy. Our principal objective was to enhance the accuracy of fuzzy C-means clustering and the random forest. To boost the model's performance, we incorporated PCA, fuzzy c-means, and the Random Forest approach. Various algorithmic combinations were employed, and the results unequivocally demonstrate that our model surpasses the original outcomes of the Pima Indian Diabetes Dataset in terms of accuracy. The diabetic prediction model achieved a remarkable accuracy of 97.40\% through the utilization of PCA, logistic regression, and K-Means. However, when employing PCA in conjunction with fuzzy C-means and random forests, an even higher accuracy of 98.96\% was attained. Empirical evidence confirms that the implementation of PCA significantly enhanced the accuracy of both the fuzzy C-means clustering approach and the random forest classifier, deviating from previous findings. To improve the model's performance, we used PCA, fuzzy c-means, and the Random Forest approach. Different algorithm combinations were used, and the results clearly show that our model outperforms the original Pima Indian Diabetes Dataset outcomes in terms of accuracy. The diabetic prediction model was improved to 97.40% accuracy using PCA, logistic regression, and K -Means. Using PCA with fuzzy C-means and random forests, however, we achieved an accuracy of 98.96%. Based on empirical evidence, it has been demonstrated that the implementation of PCA improved the accuracy of the fuzzy C-means clustering approach and the random forest classifier. These findings differ from previous findings.
Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms
Authors: Khalil Nida , Rehan Saniah , Syed Abeer Javed , Mahboob Khalid , Ali Fayyaz , Waseem Fatima
Published: January 2024

Millions of web pages carrying massive amounts of data make up the World Wide Web.  Real-time data has been generated on a wide scale on the websites. However, not every piece of data is relevant to the user. While scouring the web for information, a user may come upon a web page that contains irrelevant or incomplete information. As a response, search engines can alleviate this issue by displaying the most relevant pages. Two web page ranking algorithms are proposed in this study along with the Dijkstra algorithm; the PageRank algorithm and the Weighted PageRank algorithm. The algorithms are used to evaluate a web page's importance or relevancy within a network, such as the Internet. PageRank evaluates a page's value based on the quantity and quality of links leading to it. It is commonly utilized by nearly all search engines around the world to rank web pages in order of relevance. This algorithm is used by Google, the most widespread Internet search engine. In the process of Web mining, page rank is quite weighty. The most important component of marketing is online use mining, which investigates how people browse and operate a business on a company's website. The study presents two proposed models that try to optimize web links and improve search engine results relevancy for users.
Hire IT Application Transforming Labor Hiring with Innovative Technologies
Authors: Muhammad Saad  , Fatima Waseem  , Sarfaraz Natha  , Bilal Ahmed  , Abdul Rauf  , Baasir
Published: January 2024

In recent years, the gig economy has grown rapidly, with an increasing number of workers finding employment through online platforms such as labor hiring apps. These apps connect employers with a large pool of workers for short-term or temporary jobs, and have become a popular way for businesses to fill job openings quickly and easily. However, there are also concerns about the impact of labor hiring apps on employment and wages. This paper conducts a literature review of existing studies on labor hiring apps and the gig economy, surveys and interviews with employers and workers who have used these apps, and an analysis of app data to understand the usage pattern and user behavior. This article also explores the evolution and impact of labor hiring apps on the contemporary job market. Labor hiring apps have emerged as a disruptive force, revolutionizing the way businesses find, connect with, and employ labor. This study investigates the key features, advantages, challenges, and future prospects of labor hiring apps, shedding light on their implications for both employers and workers. Through a comprehensive analysis of existing literature, case studies, and expert opinions, this article aims to provide a holistic understanding of the dynamics surrounding labor hiring apps.
Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers
Authors: Khan Saif ur Rehman , Raza Asif , Muhammad Tanveer Meeran  , Bilhaj Umair
Published: January 2024

Breast cancer is one of the most prevalent and life-threatening forms of cancer due to its aggressive nature and high mortality rates. Early detection significantly improves a patient's chances of survival. Currently, mammography is the preferred diagnostic method, but it has drawbacks such as radiation exposure and high costs. In response to these challenges, thermography has become a less invasive and cost-effective alternative, gaining popularity. We aim to develop a cutting-edge model for breast cancer detection based on thermal imaging. The initial phase involves creating a customized machine-learning (ML) model built on convolutional neural networks (CNN). Subsequently, this model undergoes training using a diverse dataset of thermal images depicting breast abnormalities, enabling it to identify breast cancer effectively. This innovative approach promises to revolutionize breast cancer diagnosis and offers a safer and more accessible alternative to traditional methods. In our recent study, we leveraged thermal image processing techniques to forecast breast cancer precisely based on its external manifestations, particularly in cases where multiple factors are interconnected. This research employed various image classification methods to categorize breast cancer effectively. Our comprehensive approach encompassed segmentation, texture-based feature extraction from thermal images, and subsequent image classification, leading to the successful detection of malignant images. Our study harnessed the power of machine learning to create a tailored classifier, merging key components from GoogleNet, including the utilization of 2D CNNs and activation functions, with the ResNet architecture. This hybrid approach incorporated batch normalization layers following each convolutional layer and employed max-pooling to enhance classification accuracy. Next, we used a sample dataset of carefully selected images from DMR-IR to train our proposed model. The outcomes of this training demonstrated significant improvement over existing methods, with our suggested 2D CNN classifiers achieving an impressive classification rate of 95%, surpassing both the SVM and current CNN models, which achieved rates of 91% and 71%, respectively.
Comparative Analysis of Different Operating Systems used for Low-End IoT Devices
Authors: Riaz Zurabia
Published: January 2024

The Internet of Things is the emerging field that aims to connect billions of devices together over the Internet. IoT devices, divided into high-end and low-end devices. Linux-based operating systems can easily handle IoT-based high-end devices. Due to resource-based constraints that contain very little memory, developing energy for computing low-end IoT devices is difficult. In this document, the emphasis is on the detailed discussion of the operating systems that meet the requirements of IoT devices for low-end categories. A comparative analysis is carried out for the different operating systems and then the focus is placed on the operating system that comes close to Linux and is suitable for low-end IoT devices.
A Technique for Prediction Cytokines based On Statistical Moments and a Random Forest Classifier
Authors: Ashraf Adeel , Ashraf Rana Ali Rza
Published: January 2024

Research in the analysis of cytokine plays an important role because of the importance that cytokine has in the treatment and analysis of disease, but the current method for cytokine identification have numerous weaknesses, such as low affectability and low F-score. In this paper we purposed a new prediction method by consolidating the protein place explicit propensity into general type of pseudo amino acid sequences. Our predictor model has used CSM, PRIM, RPRIM, FMD, AAPIV, RAAPIV based on ANN or RFF algorithm to compute the Accuracy, Sensitivity, Specificity and MCC which are 96.28%,88.96%,99.94%,91.73% respectively using 10-fold cross validation. RFA shows 96.28% result. Our model has given the more accuracy other than research models using SVM.
Identifying Key Genes of Liver Cancer by Using Random Forest Classification
Authors: Ashraf Adeel , Roomi Muhammad Sohaib , Akram Muhammad Sohaib
Published: January 2024

Liver cancer is considered as one of the most deadly cancer. To devise a treatment which is helpful to eradicate, it is inevitable to identify potential biomarkers which are very important in the development of liver cancer. To identify the pathways and key genes we use different enrichment analysis techniques such as pathway analysis and functional analysis. To identify biomarkers we constructed a network which is named as protein protein interaction network to analyse by selecting different network nodes. Our results show that we identified those biomarkers like ESR1 and TOP2 successfully which are potential biomarkers for liver cancer. In addition to that our method can be applied to other different datasets which are for different diseases to choose key genes.
Diagnosis of Alzheimer’s Disease using Comparative Study on Machine Learning Models
Authors: Zohra Ghulam , Akram Muhammad Sohaib , Din Saif ud
Published: January 2024

The method of diagnosing and treating diseases can be improved by identifying the genes that cause diseases. Alzheimer’s disease (AD) is one of the neurodegenerative disease that slowly destroys memory as well as thinking abilities. It’s important to diagnose Alzheimer’s disease (AD) early on so that adequate treatment can be given to patient. That article compares various machine learning models for identify Alzheimer’s Disease and proves that which algorithm gives the most reliable results in detecting AD in advance. Machine learning is a backbone of technology and everything in our life related to machine learning technologies. In this study various biomarkers are developed based on different machine learning classifiers like Random Forest, K-NN, Support Vector Machine, AdaBoost and XgBoost for AD gene detection. Genome data is extracted from NCBI related to Alzheimer disease. After that features are extracted from this genome data. Then above machine learning classifiers are train on these features. Different results are obtained by using Self-Consistency test and 10 Cross Validation test. Random Forest in both test gives 100% results. KNN gives 73.17% and 86.33%, SVM gives 100% and 97% AdaBoost gives 74.02% and 87.42%, XgBoost gives 86.04%and 92.56%accuracy for self-consistency and 10 Cross Validation test respectively.
Computational Identification of Lungs Cancer Causing Genes by Machine Learning (Ml) Classifiers
Authors: Saleem Muntaha , Sohaib Akram Muhammad , Khawaja Seher Ansar
Published: January 2024

Molarity rate is increasing day by day at all over the world among both genders due to the increasing rate of lung cancer. It is a dangerous disease and usually it starts when unrestrained growth of abnormal cells start growing in lungs. The early detection of this disease has been a major challenge in the past hence, to overcome this issue many detection techniques have introduced over the time. In last decade, many Machine Learning classifiers have developed and adopted for the detection of lungs cancer. In this study, we have utilized six ML classifier such as ‘Support Vector Machine ‘(SVM) ‘K-Nearest Neighbor’ (KNN), Adaboost, ‘Conventional Neural Network’ (CNN), Xgboost and Naïve Bayes for the detection of lungs cancer causing genes. We have collected dataset from publicly available intoGene browser. This dataset consists of 2193 genes in which both tumor and non-tumor genes are included. To find, which classifier provide high accuracy of lungs cancer detection as well as lungs cancer causing genes, this study have used the above-mentioned ML classifiers and found that CNN proved to be the best classifier with 86 percent accuracy among all classifiers.
Prediction of Allergen and Non-Allergen Proteins Sequence via Chou's 5-Step Rule
Authors: Nawaz Sumbal , Fatima Kalsoom , Ashraf Adeel
Published: January 2024

Some specific kinds of proteins are responsible for the risk of immediate type I allergic reaction. Therefore, the proteins that are made to use in the consumer product should be checked for their allergic reactions before introducing them in the market. The FAO/WHO instructions for the assessment of allergic proteins depend on the linear sequence window identity and short peptide hits misclassify many proteins as allergen proteins. This study introduces the AllerPredictor model that predicts the allergen & non-allergen proteins depending on the sequence of proteins. Data was downloaded from two major databases, FARRP and UniProtKB. The results of this model were validated with the help of self-consistency testing, independence testing, and jackknife testing. The accuracy for self-consistency validation is 99.89%, for the independence testing is 74.23%, and for 10-fold cross-validation, it is 97.17%. To predict the allergen and non-allergen proteins, this AllerPredictor model has a better accuracy than other existing methods.
Bladder And Kidney Cancer Genome Classification Using Neural Network
Authors: Rana Tanzeel Sultan , Ashraf Adeel
Published: January 2024

Cancer genome classification is very important due to its importance in daily life. In few decades hundred thousand people get effected it and it cause of death for them. The major cause of late identification of cancer genome. So in our work we emphasize on three types of cancer genome which belongs to two major types which are bladder and kidney. We discuss the BLCA, KICH and KIRC. Our work explain the real time authenticity of the genome from the normal genome which are named as mutation dataset. We apply the conventional model and compare them with neural network model and found that the neural network performs very well with respect to the conventional model and the given tables also annotate its significance.
Protein Carbonylation Sites Prediction using Biomarkers of Oxidative Stress in Various Human Diseases: A Systematic Literature Review
Authors: Ashraf Adeel , Shahzadi Anam , Akram Muhammad Sohaib
Published: January 2024

Protein carbonylation is a non-enzymatic, irreversible, post translational modification (PTM). Carbonylation basically occurs due to the ROS, these species cause the oxidation of proteins and it will lead towards post translational modification of proteins known as carbonylation. In this ROS induces the carbonyl groups into the side chain of amino acid lysine (K), Proline (P), Arginine (R), Threonine (T). Carbonylation is known as a major hall mark or oxidative stress and leads to various diseases like age and age-related diseases. Different techniques and tools have been presented for detection of protein carbonylation, yet still there is no accurate result. In this systematic literature review I try to provide deep understanding of protein carbonylation sites, various techniques, comparison of tools relative to the techniques and its role in different diseases.
Identification of Hormone Binding Proteins via PSeACC
Authors: Ashraf Adeel
Published: January 2024

Hormone binding protein (HBP) is analogous to a soluble protein carrier and can interact with human hormones non-covalently and selectively. HBP plays an imperative function in the growth of life, but its role remains uncertain. The first step in advancing the study of their work and recognizing their biological process is the correct identification of HBPs. It is difficult, however, to correctly classify HBPs from via conventional biochemical experiments, due to high experimental costs and long experimental time, more and more proteins. Meanwhile, experimental methods are still labor-intensive and cost-effective to identify HBP, developing computational methods to identify HBP accurately and efficiently is crucial. In this analysis, a method based on machine learning was suggested to classify the HBP during which the samples were encoded using the optimal composition of tripeptides obtained by supporting the binomial distribution method. The suggested approach yielded an overall precision of 97.15 percent in the 5-fold cross-validation test. A new technique for recognizing HBPs is provided by this report.
Malware Injection Attacks in Resource Virtualization of Cloud Computing Environment
Authors: Rasheed Mansoor Ahmad
Published: January 2024

Cloud computing is a procedure which gives us a large, reliable and scalable infrastructure and service. Cloud computing have some features of distributed system, according to these features cloud computing also uses the features of networking. In this technique, there is large number of data sets on the internet. These data sets can be stored and accessed by a remote server. Some common and basic examples of cloud computing are Amazon, Google’s application etc. This technique provides the user to build an online application and to access that from any remote server hosted on internet. We know cloud computing system usually operate on an open network (public) so there comes security issues. The critical part of cloud computing technology is Virtualization. Virtualization refers to creating new system on the machine that is not actual but acts and performs like the actual ones. Cloud Computing and Virtualization give different services by working together. -This paper describes the malware injection attack on virtual systems working in cloud computing environment and their solutions as well. Cloud computing based on sharing technique therefore, the security is the leading crisis of this system. In this paper we discussed the diverse types of malware injection attacks and their possible solutions. Virtual machine emulators have turn out to be conventional in the examination of malicious code. This paper presents notorious attacks for the most extensively used virtual machine emulators. This paper also demonstrates newly exposed attacks on Bochs, Hydra, QEMU, and Xen (other virtual machine emulators), and describes how to protect from them.
Clustering Approaches for Efficient Radio Resource Management in Heterogeneous Networks
Authors: Farhan Naureen , Rizvi Safdar , Shabbir Amna , Memon Imran
Published: January 2024

5G telecommunication industry promises to manage and accomplish the massive data traffic and growing network requirement complexities in heterogeneous networks (HetNets). HetNets are K-tier networks and are expected to be seamlessly connected networks with robust services for users anywhere at any time. In near future, the significance of 5G/B5G cellular networks; in both indoor and outdoor environments will be greater than before and it would add up to an exhaustive level. However, as a result of the increased density of networks, a rise in interference within these ultra-dense networks (UDN) will have an alarming impact on throughput, interference and latency.  To ensure high throughput with reduced interference in UDNs a clustered architecture is required. A HetNet with clustered approach enables the network to mitigate interference effectively and achieve efficient radio resource management (RRM). In this paper, we analyzed different clustering classifications and existing clustering techniques that are used for proficient radio resource management. The centralized clustering techniques and decentralized clustering techniques are analyzed and as a result, it is assumed that improved performance can be achieved by emphasizing on hybrid clustering approaches. In addition to this, performed a thoughtful review of existing hybrid clustering techniques to achieve improved throughput and mitigate interference in dense heterogeneous networks.  Our analysis shows that improved radio resource management and increased throughput in HetNets is achieved by applying hybrid clustering techniques with reduced inter and intra tier interference.
Freelancing as an alternative Market for software Professionals in Pakistan
Authors: Abrar Muhammad Faisal , Ur Rehman Zia , Usman Muhammad , Ullah Najeeb
Published: January 2024

Researchers have already shown that job satisfaction of workers is highly associated with motivation, performance and satisfaction of workers/professionals. Conventionally workers in software industry are employed in software companies or organizations and get regular salary for their services to organization in regular office hours; however the growing use of communication technology has generated a new type of online workers, which work on project basis without affiliation with any specific software organization are known as freelancers and are continuously growing in most of the developing countries like Pakistan. This work has quantitatively analyzed the freelancers and traditional workers based on the factors already associated with job satisfaction and growth opportunities present in the field of freelancing and traditional employment in organizational setting such as company office in Pakistani context. We used online survey to collect evidences from both fields. The descriptive analysis of the evidences has shown significant differences between the two groups of workers regarding the factors such as work environment, work hours management, and job security that contribute to the job satisfaction and dissatisfaction of workers, however no significant difference has seen regarding a number of factors such as average work hour and earning amount. The resultant information of this study may help new computer graduates of Pakistan to find their way in selecting their career type and work life in the software industry of Pakistan.
Improved Classification with Simultaneous Feature Selection of Speech Signals for Parkinson's Patients
Authors: Sharif Waqas , Siddique Marina , Mumtaz Shahzad , Riaz Omer
Published: January 2024

Parkinson's disease (PD) is a brain disorder occurs due to a deficiency of dopamine hormone that regulates activities of the human body. Generally, the disease can be diagnosed by clinicians through clinical observation where they categorized PD patients on a PD assessment scale to understand disease severity in order to define a therapy/treatment plan. The clinicians have a view that this approach is not suitable for diagnosis at an early stage of the disease. Recent research outcome has shown that PD patients exhibit vocal impairment at the early stage of the disease, and this is now becoming a benchmark for early PD detection. Often researchers employ state-of-the-art speech analysis techniques that exploit various extracted features to meet the objective. An optimal set of features that best explains the problem often requires careful attention to the selection of extracted features in use. As a general practice, data analysts have a view that it is better to collect as many features as possible related to the problem but at the same time, it is also believed that the presence of some noisy features can also compromise classification ability. Our main objective in this work is to select/identify the optimal set of features to utilize for the machine learning classification models with an objective to have an improved early PD detection in patients. The selection of optimal features set will not only help clinicians to quickly diagnose PD but will also be useful to develop a better patient care strategy at an early stage of PD. In this study, various experiment are conducted to observe the most contributing speech feature to classify PD patients. The study have showed by using the Best-First feature selection approach the most optimal features from the PD dataset can be achieved. The efficacy of our approach with the optimal set of features has shown an improvement in classification with an accuracy of 92.19% that is better than the earliest reported accuracy of 86% [23] for an almost similar number of features.
Automated Psoriasis Detection using Deep Learning
Authors: Amin Nagina , Farooq Muhammad Shoaib
Published: January 2024

Psoriasis is a chronic, noncontagious skin condition that cannot be cured but its early detection can help prevent serious life-threatening complications. The high visual similarity between normal skin and psoriasis has made the detection of psoriasis a very complex task. Moreover, it can be confused with different skin abnormalities like eczema, tinea corporis, lichen planus, pityriasis, dandruff, seborrheic dermatitis. Image processing using deep learning has proven better than other approaches in this context because of its automatic feature extractions with intelligent decisions and less chances of distorted features. In this paper, automated detection of psoriasis using deep learning has been proposed. To obtain good results for a small dataset transfer learning mechanism is used in which pre-trained deep learning models are applied on a dataset to obtain the required results. Firstly, different transfer learning models are applied on our data to work on the best-obtained accuracy. Among them, ResNeXt gave the best output for an appropriate accuracy to detect psoriasis from healthy skin as well as other skin diseases. Secondly, we have worked on facilitating the development of an automated system that classifies psoriasis, lichen planus, eczema, seborrheic dermatitis, pityriasis, normal skin, and tinea corporis diseases by applying and improving the final layers of the pre-trained model. We have obtained an accuracy of 94% on test images with 2 classifiers and output to show if the input image is classified as psoriasis or not. Finally, we have also applied the classifier on 3 classes; normal skin, psoriasis, and other skin diseases, and obtained good results
A Retrospective Study for the Selection of Suitable Programming Language for the High Schools in Pakistan
Authors: Farooq Muhammad Shoaib , Tehseen Rabia , Omer Uzma
Published: January 2024

Computer programming is now increasingly becoming a compulsory subject at the high school level. The main purpose of the introduction of programming languages at this stage is to develop logic and reasoning skills that may help students in their ordinary activities. Pakistani schools have also started introducing computer programming at the high school level. GWBasic and C are two major languages being used to teach this introductory course. This paper presents the difficulties faced by the students to learn programming languages in the high schools of Pakistan while learning in any of these two languages. To this end, the content of the course has been divided into three difficulty levels. A survey based approach has been employed to get useful data from the students who have been taught this course in either of the aforementioned languages. A detailed questionnaire has been created and is filled by the student to express the ease of learning for different concepts. The collected data enables us to perform a retrospective analysis to figure out as to which language offers a smoother and easier learning curve at a given level. The results show that GWBasic is relatively easier language for the students to learn introductory course in computer programming. The accumulated data also reveals that the students struggle to learn advanced concepts that invites for a revision of curriculum or pedagogical practices being followed to teach this course.
Impact of using e-learning tools on Student’s Psychological Health during covid-19
Authors: Das Khushal , Shehryar Muhammad , Abid Fazeel , Ashraf Mohsin , Adil Muhammad , Inam Sohaima , Haq Ehtesham Ul , Mushtaq Hammad
Published: January 2024

COVID-19 is a disease generally caused by a virus called Coronavirus. The first corona case was reported in Wuhan, the City of China, whichspread worldwide and led to global health emergencies as a pandemic. Under this situation, like many countries, the Pakistan educational system implements an electronic learning (e-learning) mode due to lockdown all over the country. However, during the implementation, it has been observed that the sudden application of the e-learning mode significantly disturbs the psychological health of students. In order to address this problem, the current research work initially led an online survey through a google form to gather a novel dataset to study the psychological effect of electronic tools specifically on UMT students' goodness. A dataset of 735 responses was recorded to predict students' depression, sleeping habits, social interaction, and academic performance using e-learning tools. Further, the dataset was analyzed regarding each question's occurrences and proportions that were considered and tabulated. Secondly, multiple machine learning models such as Logistic regression, Naïve Bayes, k-Nearest Neighbours, and Support Vector Machine are considered to classify and deliberate the relationship between electronic learning and psychological illnesses. The experimental results showed that students' psychological health was significantly impacted due to the usage of e-learning tools during a pandemic. In future work, we will extend the scope of the proposed methodology to understand university students' emotional behaviour, psychological desires, and political inferring to improve electronic learning.
Augmented Reality in Healthcare Education for Human Anatomy
Authors: Zafar Adeel , Farooq Muhammad Shoaib
Published: January 2024

The study of structures in the human body is known as human anatomy. Medical students face a lot of difficulties while understanding the core concepts of Human Anatomy. To learn anatomy, Augmented Reality (AR) offers an interactive learning experience by visually constructing 3D objects in the user’s world. AR has transformed healthcare education by supporting interactive learning in a way that helps the students and other learners develop new skills by understanding different concepts in a fun and an interactive way rather than the old traditional methods. Various studies show how Augmented Reality has helped different sectors such as Healthcare, Education, Gaming Industry etc. in improving their overall standards by providing   a holistic view about different topics. To acknowledge the positive impact of AR in healthcare education in Human Anatomy, a Systematic Literature Review (SLR) is presented in this article. For this article, 34 papers were chosen through a comprehensive procedure. As per our best knowledge, no SLR is available that gives us an extensive review in this field of study. In this paper, we have presented different AR Systems and MAR applications to understand human anatomy in an interactive manner. A taxonomy diagram of the used AR techniques to learn human anatomy has also been presented. Different human organs and body parts taught through AR to help healthcare professionals and students have also been identified in this paper.
Ethical Guidelines for Artificial Intelligence: A Systematic Literature Review
Authors: Farooq Muhammad Shoaib , Tahseen Rabia , Omer Uzma
Published: January 2024

Artificial intelligence ethics is a field gaining vast functional importance in recent years. Most people assume that Artificial Intelligence (AI) could make employees redundant in future while others raise questions about ethical line of control while using AI technologies. Thus ensuring protection of data while adopting new technologies is critical and needs the prioritization of ethical standards for AI. Globalized bodies, particularly government agencies, private firms and academic institutions have made comprehensive efforts to address numerous ethical issues by writing AI ethical guidelines and holding active debates on AI ethics within and outside the AI community. This article addresses these activities in the form of a systematic literature review (SLR) with an emphasis on the recognition and practice of basic ethical concepts of AI. To the best of our knowledge, no such SLR has been conducted in this domain in past. In this SLR, research articles have been collected between the span of 2015 to 2020. A total of 784 articles have been analysed from which only 44 papers were selected for their proposed works. The analysis founds a consensus among specific standards of AI and commonly applied principles for clarity, equality, accountability, non-maleficence, and secrecy. This article presents a comprehensive review of the schemes developed and applied by the researchers for introduction of AI ethics. In this review, it has been noted that ethical standards must be integrated with all aspects of AI product life cycle.