bluuebunny/crossref_metadata_2025 huggingface dataset:
Early-Stage Heart Disease Prediction using supervised Machine Learning Algorithms
Authors: Arif Hamza , Siddique Muhammad , Aslam Naeem , Pervez Muhammad Tariq , Khan Maryam Kausar
Published: January 2024

Heart Disease is believed to be the number one killer globally, and its diagnosis has long been considered a very crucial problem. With the revolution of the modern world, it is very important to detect heart disease at its earlier stages so that patient treatment should be done effectively. Many previous researchers used Hybrid and Data Mining techniques to predict heart disease at its earlier stages, but they couldn’t get the required results. The evaluation of the Machine learning and artificial intelligence research community mainly focused on these techniques to get better results. This research paper used six supervised machine learning classifiers like Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbor and Naïve Bayes. We used two different datasets for the sample data in terms of attributes and values. We also used three different feature selection techniques to improve our accuracy by selecting the most important features. We first applied these machine learning classifiers to our proposed dataset without using the feature selection technique and computing the results. After that, we applied LASSOM, MRMR and MIFS techniques and derived the results with improved accuracies. In the end, we make a comparison table between the results that are computed with and without the feature selection technique. According to our experimental results we can say that the results accuracies computed with the feature selection technique are higher than those computed without feature selection techniques. Evaluation techniques like Confusion matrix, Accuracy, precision, Recall, F1 Score, PR Curve and ROC curve are used to measure the performance of our classifiers.  So, we conclude that if we use any of these feature selection techniques, we can conclude better results and predict heart disease at its earlier stages with improved accuracy.
Extracting the most important Discrete Cosine Transform (DCT) Coefficients for Image Compression using Deep Learning
Authors: Khan Zirak , Arslan Farrukh , Munir Faseeha , Ali Mubashir , . Shahrukh
Published: January 2024

Image compression is all about reducing storage costs and making the transmission of huge image files feasible. This paper targets lossy image compression by estimating the most important Discrete Cosine Transform (DCT) coefficients through employing deep learning. DCT basically results in the transformation of an image to the frequency domain from the spatial domain. The first few coefficients, in the frequency domain of a transformed image, have great importance. They are the most informative, while the others are of the least importance. The abilities of Multi-Layer Perceptron (MLP) and Convolutional neural network (CNN) were exploited in order to find a reasonable estimate of important DCT coefficients. The target was to get a deep neural network (DNN) for the compression of digital images that has a reduced number of DCT coefficients that is; higher compression rate and better image quality upon reconstruction and improved generalization ability. To shorten the encoding-decoding time and to fasten the training of our deep neural networks, RELUs and Tangent Sigmoid were used. Experiments performed on a large set of grayscale images shows that only 15 out of 64 total available DCT coefficients result in more than 70% image quality and have a good compression ratio of 4:1. Moreover, the quality of images upon a subjective and objective evaluation of unseen data proves that our proposed MLP achieved better generalization as compared to CNN.
The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis
Authors: Ghouri Mohsin Ghaffar , Ashraf Mohsin
Published: January 2024

Cryptocurrency is the most secure, traceable, and reliable intangible currency because it uses blockchain technology. It eliminates the middle layer of financial institutes in the traditional economic system. Because of high returns in cryptocurrencies, investors and other firms invest a lot of money. But the prices of the cryptocurrencies are uncertain. Prices of cryptocurrencies are influenced by many factors like sentiments, trading volume, and similar. Researchers are doing plenty of work to predict the accurate prices of various cryptocurrencies. However, many of these methods cannot be used in real-time. Several deep learning models such as Neural networks (NN), Long short-term memory (LSTM), and Gated recurrent unit (GRU) have been utilized by researchers for predicting the price of cryptocurrencies and yet, are unable to achieve significant results. This work combines LSTM and GRU with sentiment analysis to precisely estimate bitcoin values. We have used Root means square error (RMSE) to evaluate the model performance with and without sentiments. Empirically, we have compared the results with the other state-of-the-art models and found better results using the proposed hybrid model incorporated with sentiments.
RDED: Recommendation of Diet and Exercise for Diabetes Patients using Restricted Boltzmann Machine
Authors: Sajid Muhammad , Aslam Dr. Naeem , Abid Muhammad Kamran , Fuzail Muhammad
Published: January 2024

As per World Health Organization, noncommunicable diseases such as untimely birth, heart attacks, diabetes, and cancers are on the upswing. Diet intake that is insufficient or improper is known to cause a wide range of well-being illnesses. Due to the complexity of food components and a large number of dietary sources, it is difficult to select diets that must match one’s nutrition demands in real-time. Because of irrelevant material on proper food, patients are dependent on medicine rather than having precautionary steps in food consumption. Appropriate diet is especially crucial for persons living with chronic conditions and nutritionist food is essential for optimal health. An effective way to prevent disease is to eat a healthy nutritious diet. This study introduces the food and physical activity recommender system, which is capable of providing users with individualized and healthy nutrition recommendations based on their tastes as well as pathological medical data. Prescriptions characterize the ideal patient’s nutrition likes. In this paper, we show how Restricted-Boltzmann Machines, a type of two-layer undirected graphical model, can be utilized to describe ratings of food products. For this simple model, we provide effective learning and inference strategies that would be successfully applied to a food data set with over 100 million user-food ratings. When the predictions of the RBM model are created using different learning rates and several iterations, we attain an error rate of considerably below 0.30 percent in 50 epochs using 100 hidden nodes which fulfills our requirements. Hence, we want patients to use nutritious food rather than taking medicine to avoid an expensive trip to a physician.
Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model
Authors: Arshed Muhammad Asad , Shahzad Aqsa , Arshad Kamran , Karim Danial , Mumtaz Shahzad , Tanveer Muhammad
Published: January 2024

A brain tumor is an accumulation of malignant cells that results from unrestrained cell division. Tumors can result in crucial effects if they are not promptly and accurately recognized. Misdiagnosis can result in ineffective therapy, which decreases the patient's survival rate. The standard procedure for determining the presence of brain tumors and the type of tumors is magnetic resonance imaging (MRI). But as technology advances, it gets harder to comprehend huge amounts of data generated in an acceptable time. However, building a deep learning model from the start requires collecting enormous amounts of labeled data, which is a costly, time-consuming operation. A method to solve these issues is transfer learning of a deep learning model that has already been trained on the ImageNet dataset. In this research, the classification of brain tumors using several pre-trained deep learning models, i.e., different variations of ResNet, VGG, and DenseNet models, are being trained on a brain tumor dataset and compared. According to experiments, the ResNet50 model with a fine-tuned and transfer learning approach has achieved the highest training accuracy of 99%, validation accuracy of 96%, and test accuracy of 80%.
An Agent-based Rule Driven Knowledge Model for Epidemic Simulation: A Case Study of Lahore City.
Authors: Arslan Farrukh , Sahar Madiha , Javaid Aqib , Zaheer Awan Muhammad Danish , Rehman Ebad ur
Published: January 2024

In order to mitigate the effects of disease spread, it is necessary to understand the Spatio-temporal dynamics of epidemic spread and response evaluation. For that purpose, an agent-based heterogeneous knowledge model is synthesized using a probabilistic approach that depicts population interactions. The approach can generate a baseline population synthesis for cities such as Lahore, Pakistan where the population is quite dense and agent-based modeling becomes quite complex. An agent-based Spatio-temporal model of airborne disease is analyzed (i.e., influenza) in a metropolitan area. Such diseases result in mass casualties’ events in the populous regions, every year. Using GIS shapefiles, prior knowledge of people activities and IPUMS data, the agent base model is synthesized and an epidemic spread scenario is simulated. The proposed model can be used by healthcare officials for disease spread simulation. The model is helpful when heterogeneity semantics in population bring complexity in computations. It brings different attributes like age, sex and social status under consideration while calculating the probability of being infected as the way of interaction between people is highly depend upon them. This proposed model takes spatial factor into account as the way an infection spreads depending upon spatial distribution of infected and susceptible ones.
Application of Advance Energy Storage System in IoT and Blockchain
Authors: Saddiqi Fahad Ahmed , Ahmed Afshaar , Farhi Lubna , Ahmed Mishaal , Amin Uzma , Ahmed Manzar
Published: January 2024

In many under developing countries the demand of the electricity has been increased rapidly and the cost of the electricity is become very high. The researchers are focusing on cheap energy resources for sustainable future. Only few energy options are available, such as solar power and wind power systems. These systems depend on battery storage system because, if wind speed below desire limits no electricity will generated and same conditions are for solar system, if there will be no sun light or below the desire intensity of light, no electricity can be produced. The commercially available batteries are inefficient and average battery cost 500-1000 US dollars and warranty is around only two years for lead acid battery and three years for dry battery. Previously very limited research work has been done related to topic. The only solution is availability of efficient batteries with low cost and longer life span to address the present issue. Today the demand and utilization of these batteries has been increase so rapidly in many filed such as in energy sector and IoT application. One-way is to improve battery life and efficiency by using Nano coating, Nano wire and Nano batteries. In this paper, the technique presented to improve batteries and storage system for Smartgrid. Also the blockchain technique will be analyzed to control and energy transfer and charging IoT devices efficiently. For analyzing the proposed technique, the matlab simulation tool will be use.
Efficient Application Protection against Untrusted Operating Systems
Authors: Murtaza Hudabia , Tanweer Hafiz Abdullah , Ahmad Muhammad , Rasheed Mannan Ahmad , Rasheed Mansoor Ahmad
Published: January 2024

Commodity monolithic systems are enormous, having lots of unprotection that leads to attacks. In this era of increasing technology, relying on mobile devices and other electronic devices increases the threat from adversaries as they have the potential to obtain physical access to many computers through breaching and security theft. It is still challenging to secure data as our operating systems are less trustworthy than we expected. When the operating system (OS) is destabilized, the application data and implementation are at the attacker's mercy. Other operating systems are less trustworthy than we imagined. Therefore when the OS is unstable, data and application implementation are vulnerable to attackers. Our protective application strategy includes a secure network platform, equipment modification, and efficiency expenditure. Existing protecting application scheme has different drawbacks, such as a vast trusted computing base, hardware refinement, and high-performance overhead. To overcome these drawbacks, we present the scheme of AppShield, which has a bare-metal hypervisor-based technique in which we safely protect the data of critical applications and their execution by application localization. Due to this, we protect only critical applications, and due to this, our operating system can run without performance loss.
Automated Verification of Star-Vote in the Applied Pi Calculus
Authors: Kazm Asad Raza , Kanwal Maryam , Fakhir Muhammad Ilyas , Qasim Awais , Ishaq Atif
Published: January 2024

Security protocols form the backbone of our modern digital society. Voting is one of the most important activities in modern democratic world. Recently proposed Star-vote voting system claims to satisfy some crucial electronic voting protocol properties. In this work, we analyze properties of Star-vote electronic voting system. We are interested in privacy, fairness and eligibility properties. Intuitively, an electronic voting protocol ensures privacy if the link between voter and his vote remains secret. Fairness property holds if no partial election results can be obtained which can influence the remaining voters. Eligibility property holds if only registered voters can vote. We used proverif to prove these properties hold. To our knowledge we are conducting first automated analysis of privacy, fairness and eligibility properties of Star-vote voting system in presence of authorities behave as intruder. Ultimately, this work will help to avoid the current situation whereby many electronic voting protocols are purposed and deployed, and does not satisfy basic security property and found to be insecure.
Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant
Authors: Hassan Jawad , Malik Kaleem Razzaq , Irtaza Ghulam , Ghulam Ali , Ahmad Ashfaq
Published: January 2024

Among all the agrician products, cotton is known as “Ready Cash Crop” and it plays the significant role in the stability of the economy of a country. Therefore, it is extremely important to monitor the cotton crop from the numerous diseases. Unfortunately, sometimes human eyes not be able to analyze these diseases at earlier stage and that will affect not only the quality and also the quantity of the cotton crops. To address this early monitoring issue we proposed an interactive framework based on target feature extraction and deep learning model for cotton leaf screening to deal with these well-known dangerous diseases; Grey Mildew, Cercospora, Bacterial Blight and Alternaria. In this study we chosen our own collected dataset that contains 522 images of cotton leaves that were collected from the field (Cotton agricultural areas near the Multan city). The performance evaluation matric indicates the algorithm secure; 85.42% overall accuracy, 0.8542 precision, 0.8542 recall, 0.854 F1 score and 0.817 kappa coefficient indicates the generalization and acceptability of the model. The proposed framework not only assists the agronomist but also the farmer because of early identification of diseases from cotton crop and to avoid from the massive loss. It make better decisions for cotton crop management and contributes in the sustainability of the economy.
A Systematic Literature Review on Software- refactoring Techniques, Challenges, and Practices
Authors: Akhtar Salwa Muhammad , Nazir Makia , Ali Arshad , Khan Ahmad Salman , Atif Muhammad , Naseer Mudassar
Published: January 2024

Software-refactoring improves the quality and reduces the complexity during the whole life cycle of the software system. The objective of this work is to elicit the existing challenges and refactoring techniques and practices to counter those challenges. This study presents a systematic literature review based upon the scientific literature published since January 2000. After rigorously applying the SLR protocol defined by Barbara Kitchenham, we identify 36 articles discussing software- refactoring challenges, techniques, and practices. We point out complexity, understandability, and maintainability as three challenges of traditional software engineering that are resolved by software-refactoring. This SLR identifies assertions, program slicing, graph transformation, software metrics, and metamodeling, as five commonly used refactoring techniques. Moreover, eight main challenges of software-refactoring, namely determining refactoring and code location, selecting refactoring type and technique, managing refactoring dependency, consistency maintenance, refactoring ordering and increase in testing cost, are also highlighted. The findings of this work suggest that software-refactoring techniques can resolve challenges of traditional software engineering.
Energy Trading in P2P Network by using Blockchain in Smart Grid
Authors: Ahmed Afshaar , Ali Agha Yasir , Ahmed Mishaal , Khan Abdul Lateef , Ahmed Manzar , Amin Uzma
Published: January 2024

In recent years, the dramatic increase in demand of electricity utilization has introduced various environmental and sustainability concerns for Conventional Energy Grid based on fossil fuels, which driving the global energy manufacturers to adapt new efficient and resilient energy systems. The conventaional Grid is based on centralized system suffer from power losss, theft cases and cannot adopt new power systems properly. Presently the focus of the scientist and researchers is on the Clean Energy resources and Smart Grid, which has potential to address the contemporary problems found in conventional energy system. On the other side , Smart is a new technology, which allows the integration of clean energy resources such as wind, solar, geothermal, hydro, biomass, and tidal energy systems, ensuring energy availability at all times. The quintessential purpose of renewable technologies is to contrive a sustained community. Nevertheless, it has become a challenging concern for Smart Grid to integrate and coordinate enormous consignment of expanding users and connections. Consequently, blockchain has emerged as a resilient technology in various industries offering data integrity, security, building trust between parties, data immutability, and secure communication. These feature of blockchain making it an auspicious technology for decentralized Smart Grid and Renewable Energy criterion. In this research work, a blockchain based energy trading modal using Peer-to-Peer transactions management is presented. The main goal is to investigate an energy-trading model based on crypto wallets as a digital currency using smart grid and renewable energies. Moreover, contemporary issues of industrial and commercial communities adopting renewable Energy system will be address.
Fuzzy smart framework for diagnosis of liver disorder in Rheumatoid arthritis
Authors: Shakil Marrium , Saleem Darakhshan M , Khan Kashif Mehboob , Shams Sarmad , Farhi Lubna
Published: January 2024

Despite most of the centralization techniques used in medical diagnosis, a right finding is yet considered a priority. Most of this condition is due to the fact that clinical issue requires both expertise and knowledge in managing with complexity. In this paper, we intend a fuzzy expert framework for studying and examining the risk of anemia and liver dysfunction due to the usage of drugstherapy (DMARDS) among  patients in Rhematoid arthritis. The data illustration of this system is given from a significant level, taking into account the recorded data about signs and symptoms in rheumatic patients as well as the clinical appraisal. This framework imitates the professional doctor's behavior. The system predicts the risk of anemia and liver dysfunction in patients treated with DMARDS for rheumatoid arthritis.The framework is designed in such a manner that the patient can access it individually and has correlation with other normal indicative frameworks is quicker, less expensive, and furthermore more liable and exact.
Fake News Detection in Urdu using Deep Learning
Authors: Rauf Farah , Irfan Roha , Mushtaq Lyba , Ashraf Mohsin
Published: January 2024

An increase in the volume of false information circulating as a direct consequence of the rise in the growth of social media has an effect of misguiding the general population. Therefore, a mechanism for identifying fake news is required to prevent such repercussions. Almost all of these already existing algorithms for detecting fake news work with resource-rich languages such as Spanish and English; however, few techniques can work for resource-constrained languages such as Urdu. The study aims to identify instances of fake news written in Urdu by applying deep learning and machine learning methodologies. We use the MuRIL and T5 models for the implementation process because these models were developed specifically for Urdu and Hindi language recognition. The newspaper articles included in the valid subset derived from credible news sources, and the accuracy of these items have been checked by hand. Inside the misinformation subsection, the problem of how challenging it was to discover fake news was overcome by employing experienced reporters who were native Urdu speakers and instructing them to compose deceptive news items purposefully. It has allowed the researchers to overcome the problem. The dataset covers various subjects, including business, sports, health, showbiz, and technologies. We have carried out baseline classification to use our Urdu database as a standard for other datasets. The experiments with the various systems have indicated that the MuRIL model significantly improves over the other models, such as the T5 Model, and attains an average F1 score of 0.96 and a validation accuracy of 0.83. These results have been determined based on the findings of the experiments.
Comparison of Clean Energy Trading Method in Microgrid by using Blockchain Technique
Authors: Ahmed Manzar , Ali Agha Yasir , Rehan Muhammad , Ahmed Afshaar , Amin Uzma , Ahmed Mishaal
Published: January 2024

The old conventional energy distriution system cannot fulfill the demand of the prosumers in providing secure energy trading and with minimum losses in energy transmission. Because it is centralized system and user has no freedom to trade energy without involving the third party. In conventional grid there are lot of issues such as controlling the losses and forecasting the demand and reliability. One major issue is theft of electricity and losses. The new blockchain system has potential to provide secure energy trading in network without involvement of third party. The combination of blockchain and Microgrid has all features to resolve the issues related to the current grid such as controlling power, Forecasting the demand and reliability. In this paper the combination of Microgrid and blockchain for trading energy will be analyzed and energy trading method in P2P Network based will be investigated to find best method for trading enegy in network. For the simulation the matlab tool will be used.
Hybrid technique to predict the position of Vehicle
Authors: Hussain Muzaffar , Farhi Lubna , Rehan Muhammad , Safwan Muhammad , M. Saleem Darakhshan
Published: January 2024

Utilizing wireless networking to estimate the position of vehicles on the road is an important aspect of vehicular safety. It can be used to predetermine on-coming collisions and warn the drivers with ample time to either prevent the crash or minimize damage. The proposed methodology takes advantage of multipath effect by using the hypothesis test to detect a disturbance in the vehicle position estimate. The benefit of this technique is that it reduces position estimate error by integrating all of the vehicle’s data with Time Difference of Arrival (TDOA) and then passing that information through a Kalman filter to smooth the range data thereby decreasing Non-Line of Sight (NLOS) errors as well. The proposed technique accounts for error in the GPS data and reduces random error influences to significantly improve precision in position estimation and accuracy. The simulations showed that the Kalman filter improved accuracy by 5.45% and precision by 3.56% on an average as compared to TDOA position estimation without Kalman Filter.
Empirical Investigation on Blockchain Interoperability
Authors: Khan Kashif Mehboob , Fahim Aabira , Saleem Darakhshan M. , Jokhio Marvi
Published: January 2024

With the emerging employment of blockchains in different fields a need for blockchain intercommunication has arisen but there is no set standard yet for blockchain development, adoption and implementation due to which its interoperability has become a challenge. Interoperability refers to the mechanism of exchange and utilization of information between two software or computer systems etc. In blockchain, interoperability is the process of data creation, transfer and storage between two blockchains or blockchain applications. Blockchain interoperability is complex as every blockchain may have a different implementation platform and protocol for consensus mechanism. Bringing together two different blockchains and enabling communication between them without modifying their underlying implementation structure is a challenge today. There has been ongoing research in this domain to achieve interoperability in blockchains effectively. Its importance is evident from the fact that blockchain interoperability is vital for promoting scalability which is another research challenge presently. Apart from this, blockchain interoperability also promotes data privacy, application flexibility and portability and provides new opportunities in business. In this paper we have discussed in detail the three approaches and the solutions they provide for implementing blockchain interoperability. An empirical based analysis has been used to strengthen our methodology, which takes into consideration the selection of known & established blockchain network with state-of-the-art tools and technology. In order to have seamless communication across different chains, light clients (representing the respective chain) have been enabled to store each other’s information such as protocol version etc. In this way, the handshake between both the chain has resulted in a successful IBC (Inter Blockchain Communication) inside the Cosmos environment. It is concluded that although blockchain interoperability is being implemented today, this implementation is highly restricted to specific organizations or software tools. Moreover interoperability between two different blockchains is still an ongoing challenge. This study will assist the future work in the domain of blockchain interoperability as it makes the understanding and implementation of blockchain interoperability easier.
AOPs-XGBoost: Machine learning Model for the prediction of Antioxidant Proteins properties of peptides
Authors: Rahu Sikander , Ghulam Ali , Khan Swati Zar Nawab , Arshed Jawad Usman , Malik Muhammad Shahid , Khan Nauman
Published: January 2024

Abstract Antioxidant proteins are essential for protecting cells from free radicals. The accurate identiﬁcation of antioxidant proteins via biological tests is difﬁcult because of the high time and ﬁnancial investment required. The potential of peptides produced from natural proteins is demonstrated by the fact that they are generally regarded as secure and may have additional advantageous bioactivities. Antioxidative peptides are typically discovered by analyzing numerous peptides created when a variety of proteases hydrolysis proteins. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. We suggested a machine-learning model that we named AOPs-XGBoost, built on sequence features and Extreme Gradient Boosting (XGBoost). We used 10-fold cross-validation testing was performed on a testing dataset using the propose. AOPs-XGBoost classiﬁer, and the results showed a sensitivity of 67.56%, speciﬁcity of 93.87%, average accuracy of 80.72%, mean cross-validation (MCC) of 66.29%), and area under the receiver operating characteristic curve (AUC) of 88.01%. The outcomes demonstrated that the XGBoost model outperformed the other models with accuracy of 80.72% and area under the receiver operating characteristic curve of 88.01% which were better than the other models. Experimental results demonstrate that AOPs-XGBoost is a useful classiﬁer that advances the study of antioxidant proteins.
The Design, Use and Impact of Cloud Computing During the Covid-19 Crises
Authors: Jamali Mujeeb Rehman , Kansro Nadeem Ahmed , Chandio Shahmurad , Rajper Ghulam Nabi , Shah Syed Akbar Ali
Published: January 2024

The incidence and severity of cybercrime have increased recently, and worries about personal data protection are widespread. Cybercriminals are taking advantage of the COVID-19 pandemic's crucial reliance on cloud computing emerging virtual environments by organizations and individuals. One of the biggest issues is the security of data that is stored and transmitted over the cloud. It is crucial to protect the privacy of data stored by Cloud Service Providers since in the Cloud, private and confidential information is kept on external servers and hidden from Cloud Service Users. Concerns regarding the safety of personal data are pervasive, and cybercrime has lately grown in frequency and severity. The various researchers presented their work of vulnerabilities of the data security over the cloud environment and provides encryption data at rest with symmetric key cryptography where the same secret key is used for encryption and decryption. This research takes a different strategy than that which has previously been suggested by other researches. An application-level security is proposed instead of data at rest encryption to safely store data at rest. In the public cloud computing environment, our developed system guarantees the privacy and confidentiality of data while it is at rest. In order to guarantee data security against hostile infiltration, the system also ensured that data must be safeguarded during, and after a transaction while it is being stored on the cloud storage. It can be inferred from the result that our system, when compared to other symmetric algorithms with less consistent behavior and scatter of value from mean, took less time to perform cryptographic operations.
Identifying Cancer Patients at risk for Heart Failure using deep learning models
Authors: Anis Muhammad , Majeed Adnan , Khalil Mudassir , Aslam Naeem
Published: January 2024

The cardiotoxicity that may occur as a side effect of cancer treatments has emerged as a significant problem. Cancer patient’s quality of life may be improved if those at risk of cardiotoxicity are identified early and given prophylactic treatments before receiving cardio toxic drugs. The advancement of deep learning will help to support medical practitioners in their ability to make accurate. This study will focus on predicting the enhancement of heart failure in cancer patients. The purpose of this research is to determine whether historical data from electronic health records can accurately predict the occurrence of heart failure in cancer patients. We investigated deep learning algorithms by applying them to 300 cancer patient’s dataset drawn from the Seer database. We determined that there were a total of 300 eligible cases and matched them with controls according to gender age and the primary cancer type etc. Results from the tests suggest that techniques         based on deep learning may effectively capture clinical characteristics linked with heart failure in cancer patients.
Classification of Skin Disease using Machine Learning
Authors: Ahmed Azka , Ahmad Hafsa , Khurshid Mohsin , Abid Kamran
Published: January 2024

Erythemato-squamous disease (ESD) is one of the dermatology field's complex diseases. Due to its common morphological features, it is challenging to diagnose and generally produces inconsistent results. In addition, the physician's expertise was used to make the diagnosis based on the observed symptoms. The accurate classification of erythemato-squamous disorders is one of the dermatology field's problems that need attention, and to help with this issue, by using clinical and histopathological data, this tool will differentiate the six classes of ESD. In this research, we have applied 3 different machine learning algorithms as base models i.e. Random Forest, Decision Tree, and Naïve Bayes to classify the ESD and 5 Ensemble Meta techniques such as Voting classifier, average classifier, Stacking, boosting, and bagging classifiers to measure the accuracy. In comparison to other classifier methods, the ensemble technique employed on dermatology dataset, original dataset and clinical feature extraction to identify which model performs better on both cases. The ensemble method provides a more precise and accurate prediction of skin diseases.
Evaluation of Deep Learning Approaches for Sentiment Analysis
Authors: Saqib Sheikh Muhammad , Naeem Tariq , Ahmad Shakeel , Sulaiman Alorﬁ Almuhannad
Published: January 2024

Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models.  Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.
Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey
Authors: Haroon Kinza , Minhas Sidra , Sabahat Nosheen , Nassrani Samson
Published: January 2024

Mental health is recognized as a non-communicable disease that impairs human lives, sometimes beyond recovery. While everyone is at risk of developing a mental illness, adolescents are more prone to it due to various factors like hormonal changes, study pressure, social pressure, etc. If mental health goes ignored at this stage, it can cause serious, even fatal problems later on in life, which not only impacts a family but also the young workforce of a country. Hence, constant efforts are being made for the early detection of mental disorders so they can be treated better. Early prediction of mental health issues is a classic machine learning problem relying on patient history and data. In this survey, we discuss a total of 22 previous research papers based on machine learning algorithms and other statistical analysis tools employed for the said task and compare their efficacy. The research papers are categorized into different mental health disorders such as 1) Methods for predicting Depression and Anxiety 2) Methods for Suidial Prevalence 3) Methods for Predicting Autism Spectrum Disorder (ASD) 4) Methods for Predicting Substance Abuse among adolescents. On the basis of accuracy, the performance of machine learning prediction models was compared. CNN models, Random Forest, and XGBoost generally performed better than other models. There is centralized research in Pakistan on mental health based on machine learning so SPSS and other tools are mostly used for data analysis. The findings suggest that Machine learning algorithms can be effective for classifying and early predicting high-risk factors among adolescents
A Survey on Security Issues and Attacks of Fog Computing
Authors: Qureshi Ramsha , Asad Muhammad , Tunio Saima , Qureshi Sirajuddin , Ahmed Mughees , Ghulam Ali
Published: January 2024

There is a link between the cloud and the Internet of Things (IoT). The layer that makes up the dispersed network environment is exactly what it is. Cloud computing is brought out to the edge of the network through the type of networking topology referred as fog computing. Users can benefit greatly from fog computing. Fog's primary role, similar to cloud computing, is to allow people mobility. Fog computing is becoming more and more popular, whereas at the same time, security dangers are growing every day. Users' identification & verification are crucial. The fact of fog computing cannot effectively utilize the security and privacy solutions provided by cloud computing must be emphasized. The risks, issues, and solutions linked to security in fog computing are outlined throughout this study. The poll then includes information on ongoing research projects as well as open security and safety concerns for fog computing.
Prioritization of Exigency Services in Multi-Agent Transportation Systems
Authors: Fakhir Muhammad Ilyas , Qasim Awais , Hussain Saira , Baig Shuja ur Rehman , Bilal Muhammad , Munawar Adeel
Published: January 2024

Multi-agent system helps in achieving a single global goal by working on different tasks in a distributed environment. This research presents a new framework for handling the prioritization of exigency services in an urban transportation system. Prioritization in exigency services needs to be handled for vehicles like Ambulance, Police Mobile, Fire Brigade, Bomb Disposal Squads, Search and Rescue vehicles, Turntable Ladder, and other similar vehicles. In the proposed framework a single ARTIS agent with four In-agents is deployed at each signal node. In-Agents at different nodes share information about the exigency with the ARTIS Agent that analyses the input data and determines the types of conflict that might cause delays in emergency services provision. We operate the signal based on their priorities for exigency vehicles with different priorities. In case they have the same priorities then we use the lane congestion and vehicle wait time to operate a signal. We demonstrate the application of our proposed approach using different cases of a traffic case study.
An Improved Framework for Sindh School Monitoring System Android App
Authors: Halepoto Imtiaz Ali , Gul Feroz , Memon Fayaz Ahmed , Saeed Umair , Hussain Majid , Zardari Baqir Ali
Published: January 2024

Sindh government has presented a system for observing schools called the Sindh School Monitoring System (SSMS) Framework. One of the part of the system is SSMS app, which is based on Android. SSMS app is widely used in the Sindh province in order to monitor the school with major focus on attendance. The SSMS app has increased the system performance in terms of attendance, however several flaw are present in its current framework. This paper identifies the key issues in the current framework such as identification and verification of Monitoring assistant (MA), school search options and SMC, teacher performance evaluation, reporting, curriculum, student performance evaluation and census, school building details, SNE, school amenities, GR register, NADRA verification, rights of MAs and online reporting options in the app. The changes are proposed in the existing framework, for the said key issues, which could improve the overall system performance. In order to validate the key findings and proposed changes in the existing framework a questionnaire has been prepared and evaluated from the SSMS app users. All the app users validated the proposed changes in the framework.
Roman Urdu Sentiment Analysis of Songs‘ Reviews
Authors: Qureshi Muhammad Aasim , Asif Muhammad , Khan Muhammad Farrukh , Kamal Asad , Shahid Bilal
Published: January 2024

To process Natural Language reviews using Machine Learning techniques is known as Sentiment Analysis. It is a way to categorize people's opinions, sentiments, and attitudes towards a specific entity. Due to easy access to the internet and smart devices, people are becoming habitual in posting reviews about any specific entity/product, they use. These reviews are very helpful for all types of users in decision-making. In the past, most of the work in Sentiment Analysis was carried out on resource-rich language but very little literature is witnessed on resource-poor languages. Very few efforts have been made to build language resources to process the Roman Urdu language. This research targets to perform Sentiment Analysis on Urdu (i.e. source-poor language) in Roman script. For this purpose, the dataset is generated from the comments on songs. Three songs from the Sub-continent music industry opt from YouTube. After pre-processing the reviews, Roman Urdu reviews are analysed using Naïve Bayes, KNN, Decision Tree (ID3) and ANN. Naïve Bayes outperforms the other classifiers and achieved 82.41% results in terms of accuracy.
Agent-based Modeling of COVID-19: A case study of Hyderabad city
Authors: Mangrio Iqbal Khatoon , Dewani Amirita , Meghji Areej Fatemah , Bhatti Sania
Published: January 2024

This research aims to design and develop an agent-based model (ABM) that can simulate the COVID-19 outbreak, presenting the interaction dynamics and impact of various control strategies imposed in the city of Hyderabad, Pakistan. The observations include how ABM controls the disease outbreak depending on different scenarios. The individuals are visually represented in the form of agents that can interact with each other and can make decisions depending on the situation. The observations are made by studying the dynamics of the behavior of individual agents, and their movements from one compartment to the other. The ABM model is developed in NetLogo and simulates various hypothetical scenarios that reflect the real behavior of the virus’s interaction with humans and their environment while adjusting medical, social, and demographic parameters. The study results provide details on how restricting the social interactions between individuals and their movement decreases the rate of the spread of the virus.
Sentiment Analysis of Omicron Tweets by using Machine Learning Models
Authors: Fazal Unaiza , Khan Muhibullah , Maqbool Muhammad Sajid , Bibi Hadia , Nazeer Rubaina
Published: January 2024

The COVID-19 epidemic has been affecting a lot of individuals worldwide since 2019. It is emerging as an infectious disease that set off a disaster with far-reaching effects on things like education, economics, and health. During the coronavirus outbreak, new COVID-19 mutations such the Beta, Delta, and Omicron variants emerged, terrifying and alarmed the population. Around 6 million people reportedly died as a result of COVID-19 variations, according to World Meter. The SARS-CoV-2 omicron strain was initially identified in South Africa on November 24, 2021, and it has since spread to more than 57 nations. In this essay, we examine how people feel and act toward the omicron variation. On Omicron, we proposed an approach for determining sentiment analysis for tweets from Twitter. The analysis of Twitter data's sentiment has a lot of potential. In the intended methodology, we extract the best characteristics from the Omicron tweets using NLP techniques in Python, resulting in a dataset that can be used to train the Models. The produced dataset was employed by four ML Classifiers, including “Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)”, to accurately categorise users' emotional behavior into three categories: neutral, negative, and positive. The Class Neutral receives the best score and the Class Negative receives the lowest score based on the accuracy of the forecast level.
Diabetic Retinopathy Identification from Eye Fundus images using Deep Features
Authors: Mubashra Ayesha , Naeem Ahmad , Aslam Dr. Naeem , Abid Muhammad Kamran , Haider Jabran
Published: January 2024

Diabetes  mellitus can cause diabetic retinopathy (DR), which affects the blood vessel networks in the retina of the eye. The symptoms of diabetic retinopathy may be missing or minimal. For the majority of diabetes patients, a serious problem can damage their vision. It takes a lot of effort for competent doctors to identify lesions in the color-fundus pictures that can be used to accurately diagnose the illness required for diabetic retinopathy diagnosis. The existing Diabetic retinopathy therapies can only slow or delay vision degeneration, highlighting the significance of routine scanning with very effective automatic detection techniques to identify patients early. Therefore, early symptom detection may help prevent blindness. The proposed work aimed to create an automated model for recognizing the initial stages of DR detection using retinal pictures. This research paper presents a novel approach to the multi-classification of Diabetic Retinopathy (DR) based on a combination of deep learning and machine learning techniques. Our proposed model incorporates a Convolutional Neural Network (CNN) with an attention mechanism, allowing for the assignment of weights to extracted features depending on their relevance to the classification task. We employ Non-Negative Matrix Factorization (NMF) to further optimize feature selection to identify the most informative features from weighted representations. Then, the input features are classified using a machine learning classifier based on severity levels. The proposed model is assessed using four distinct ML classifiers (Support Vector Machine (SVM), Decision Tree, Naive Bayes, and KNN) and two publicly accessible databases (DDR and APTOS-Kaggle). For model evaluation, FPR, Specificity, Sensitivity, Precision, false positive rate, and Accuracy are selected.  The proposed model's accuracy on DDR is 89.29%, whereas Aptos Kaggle's accuracy on DR grading is 84.1%. KNN classifier performed better than all other classifiers on DDR and Aptos Kaggle datasets with an average accuracy of 89.55%, and 85.78 %respectively for multiclass classification. The findings of the evaluation and experimentation demonstrate that the suggested approach is effective for DR grading accuracy and disease diagnosis at an early stage.