bluuebunny/crossref_metadata_2025 huggingface dataset:
Financial Prices Prediction of Stock Market using Supervised Machine Learning Models
Authors: Rehman Muhammad , Fuzail Muhammad , Abid Muhammad Kamran , Aslam Naeem
Published: January 2024

The process of predicting stock market movements may initially appear to be non-statistical due to the multitude of factors involved. However, machine learning techniques can be utilized to establish connections between past and present data, enabling the training of machines to make accurate assumptions based on the information. By effectively linking historical data to current data using machine learning, it becomes possible to make precise predictions regarding stock performance. These predictions can lead to substantial profits for individuals and their brokers. Traditionally, stock market predictions have exhibited chaotic patterns rather than randomness, which is why a thorough analysis of a market's historical data allows for predictions to be made. Machine learning offers an efficient means of modeling such processes. By closely aligning market predictions with actual values, the analysis's accuracy can be raised greatly. The field of stock prediction has seen a growing interest in machine learning among researchers due to its effectiveness and precision. Regression-based models are commonly employed when the objective is to forecast continuous values based on independent variables. To predict stock market closing prices for the upcoming ten to fifteen days, we used SVR, RF, KNN, LSTM, GRU, and LSTM with GRU in our study. In regression modeling, the R-squared (R2) value represents the percentage of difference explained by the independent variable(s). A higher (R2) value near to 1 indicates better performance. Our experiments yielded R2 values of 0.832, 0.832, 0.574, 0.838, 0.825, and 0.815 for SVR, RF, KNN, LSTM, GRU, and LSTM with GRU, respectively. Consequently, the most effective model for correctly predicting stock market closing prices is the LSTM learning model, which had the greatest R2 value of 0.838.
Review of Artificial Intelligence-based COVID-19 Detection and A CNN-based Model to Detect Covid-19 from X-Rays and CT images
Authors: Ahmed Mushtaq , Gilanie Ghulam , Ahsan Muhammad , Ullah Hafeez , Sheikh Faseeh Abid
Published: January 2024

Various diseases are rising in the world in different regions. Each disease is diagnosed through its signs, & symptoms, and is cured accordingly. Some persons have immunity to fight against such diseases, but most of the persons become the victim of these diseases. The epidemic in China triggered by a novel coronavirus (Covid-19) presents an unprecedented danger to general safety, worldwide. Covid-19 has a more rapid transmission rate. A speedy symptomatic standard check to identify the infectious disease is required to prevent its spread. In an existing situation, testing kits of Covid-19 are available in less quantity and they require significant time to produce outcomes. The purpose of this research is to explore recently reported techniques for automated identification of Covid-19 from medical images and to report an efficient method for the detection of Covid-19 from digital X-Ray and computed tomography images. The proposed model can assist in the identification of Covid-19 at its initial level in lesser time. Publically available and locally developed datasets have been used for research and experiments. The highest classification accuracy achieved through the reported model is 99.40%.
Enhancing SCTP Performance through the Selection of Appropriate Retransmission Policies
Authors: Halepoto Imtiaz Ali , Halepoto Fouzia , Memon Fayaz Ahmed , Bhangwar Ali Raza , Zardari Baqir Ali , Iqbal Shahid
Published: January 2024

The Stream Control Transmission Protocol (SCTP) is a reliable transport protocol that provides message oriented communication services between applications. One of the critical functions of SCTP is to ensure reliable delivery of data by detecting the lost or missing packets due to transmission errors. Once the errors are detected the SCTP uses retransmission policies for immediate retransmission of data along the same or alternate path. However, the performance of SCTP retransmission policies can significantly impact its efficiency and reliability in different network conditions. In this paper, we analyzed three retransmission policies of SCTP that are (1) CWND, (2) SSTHRESHOLD and (3) LOSSRATE, and evaluated their performance in terms of network bandwidth, propagation delay and packet loss. We conducted simulations using the NS-2 network simulator and evaluated the performance of each policy under different network conditions and in each simulation the impact on throughput is analyzed. From the simulation results, the retransmission policy that uses loss rate parameter (LOSSRATE) for the transmission of data outperforms the retransmission policy that uses parameters such as congestion window (CWND) and the slow start threshold (SSTHRESHOLD). The analysis on the obtained results provides valuable insights into the tradeoffs between different SCTP retransmission policies and can help network administrators and application developers optimize SCTP performance in different network environments.
An Efficient approach for Firearms Detection using Machine Learning
Authors: Rahoo Aamna , Alvi Fizza Abbas , Rajput Ubaidullah , Halepoto Imtiaz Ali
Published: January 2024

Each year, there is a significant number of people impacted by gun-related violence globally. To address this issue, we have created a computer-based system that can automatically identify firearms, specifically pistol. Recent advancements in machine learning has shown success in the fields of recognition and object detection. Our system utilizes the You Only Look Once (YOLO V3) object detection model, which was trained on a personalized dataset. Our training results indicate that YOLO V3 outperforms both traditional convolutional neural network (CNN) models and YOLO V2. Notably, our approach did not require high computation resources or intensive GPUs to train our model. By incorporating this YOLO V3 model into our security system, we hope to rescue lives and decrease the occurrence of manslaughter or mass killings. Moreover, detecting weapons or other dangerous materials and preventing harm or risk to human life could be accomplished by integrating this system into sophisticated surveillance and security robots.
Decision Support System for early Diagnosis of Heart Diseases
Authors: Ahmed Rubaisha Waqar
Published: January 2024

Heart disease remains a leading cause of death worldwide, emphasizing the critical need for early diagnosis to enable effective treatment and management. Decision support systems (DSS) have the potential to significantly enhance the accuracy and efficiency of heart disease diagnosis. An advanced DSS designed for early diagnosis can provide healthcare professionals with essential information, expert advice, and treatment recommendations based on comprehensive patient data. By gathering information from various sources including medical history, risk factors, and test results, the DSS analyzes and processes the data using established diagnostic criteria and the latest medical research. The DSS then generates a diagnosis or a list of possible diagnoses, along with appropriate treatment recommendations, empowering healthcare professionals to make well-informed decisions and deliver more effective patient care. Incorporating machine learning algorithms into the DSS can further enhance its accuracy and efficiency. By training the DSS on extensive patient datasets, it can identify patterns and make predictions based on new patient data, ultimately leading to improved decision-making and better patient outcomes. In conclusion, the utilization of a DSS for heart disease diagnosis holds the potential to revolutionize the field by providing healthcare professionals with vital information, expert guidance, and treatment recommendations, thereby enhancing the accuracy, efficiency, and overall outcomes of heart disease diagnosis and management.
RLTD: A Reliable, Link Quality, Temperature and Delay Aware Routing Protocol for Wireless Body Sensor Networks
Authors: Iqbal Shahid , Raza Bhangwar Ali , Ahmed Adnan , Ahmed Fayaz , Awais Muhammad , Hussain Abrar
Published: January 2024

The miniaturization of biomedical sensor nodes has paved the way for remote patient monitoring using wearable wireless sensor nodes. The network of such invasive and non-invasive sensor nodes is most commonly known as Wireless Body Sensor Network (WBSN). The healthcare applications of WBSN are delay sensitive and of critical nature, therefore, it require reliable and timely dissemination of patients’ critical data to the remote location to ensure Quality of Service (QoS). However, poor link quality (LQ) may affect QoS and result in higher transmission delay, loss of critical data packets data corruption, packet retransmissions, thereby, degrading network performance and compromising the patient’s privacy and confidentiality. Furthermore, the in-vivo wireless sensor node produces electromagnetic radiations which are absorbed by the human body thereby, causing temperature rise around the implanted sensor node. The prolonged communication of such in-vivo nodes creates a hotspot node that results in sensitive tissue damage. Therefore, ensuring hotspot-free communication is a necessity in WBSN.  Considering such issues, we propose a Reliable, Link Quality and Temperature aware routing protocol (RLTD) for delay-sensitive application of WBSNs that route data through reliable nodes and good quality links to the destination node. The efficacy of the proposed (RLTD) routing protocol is confirmed by comparing results with the well-known thermal-aware protocols for WBSN.
Using Educational Data Mining to Predict Student Academic Performance
Authors: Meghji Areej Fatemah , Shaikh Farhan Bashir , Wadho Shuaib Ahmed , Bhatti Sania , Ayyasamy Ramesh Kumar
Published: January 2024

An educational institution's primary objective is to create a learning environment that enhances student academic success by mitigating academic failure and promoting higher performance. In order to accomplish this, the institute needs an effective mechanism for quickly identifying students’ performance, in particular students at the risk of falling behind or failing a course. Using the classification approach of educational data mining, this study utilizes student descriptive, behavioral, and attitudinal data to predict academic performance at an early stage during a semester. Specifically, this study makes use of ruled-based, decision tree, function-based, lazy, multilayer perceptron, and probabilistic classification techniques for early student performance prediction. The models generated by several classifiers exhibited good performance with the model generated by the Random Forest classifier exhibiting an accuracy of 93.40% and a Kappa score of 0.9160. The experimental results of the study indicate the effectiveness of using a set of descriptive, behavioral, and attitudinal attributes to predict student performance at an earlier stage during the conduct of a semester.
Smart Agricultural Genetic Divergence Pattern Estimation of Morphological Traits in Cotton
Authors: Rajput Muhammad Arslan , Javeria Fatima Javeria , Noor Dua , Chang Ameer Hussain , Channa Zulqarnain , Mazari Faisal Nabi
Published: January 2024

Cotton (Gossypium hirsutum L.), an important agricultural fiber crop belonging to the Malvaceae family, exhibits wide genetic diversity that requires thorough investigation for the development of climate-smart cotton. This study aimed to assess the genetic variation of cotton varieties in relation to yield-related characteristics. A total of fifty genotypes were sown at the Cotton Research Institute (CRI) in Multan using a randomized complete block design (RCBD) with two replications. The row-to-row and plant-to-plant distances were maintained at 75 cm and 23 cm, respectively. Data were collected for various morphological traits, including plant height (PH), monopodial branches per plant (MO), sympodial branches per plant (SY), number of nodes (NO), boll length (BL), boll width (W), boll weight (BW), total boll weight per plant (TB), and seed cotton yield (SCY). Maximum values were recorded for PH (109.40 cm), MO (8.0500 branches/plant), SY (25.100 branches/plant), NO (41.550 nodes), BL (41.750 mm), W (41.300 mm), BW (3.9500 mg), TB (33.750 g), and SCY (95.400 g). ANOVA results indicated significant differences among all the genotypes. Positive and significant correlations were observed between PH, SCY, and BL, demonstrating the successful utilization of selection criteria based on these traits to improve cotton yields. Cladogenesis studies revealed that class I, II, and III were represented by FH-183, VH-281, and AGC-2, respectively, exhibiting superior genetic potential in terms of morphological traits. Principal component analysis (PCA) demonstrated that 81.88% of the total variance was primarily attributed to traits such as SY, TB, PH, SCY, and BW, with the first five components having eigenvalues greater than 1. These findings provide breeders with valuable insights into selecting desirable characteristics for cotton varieties.
A Novel Face Spoofing Detection Using hand crafted MobileNet
Authors: Zahra Sayyam , Khan Mohibullah , Abid Kamran , Aslam Naeem , Khera Ejaz Ahmad
Published: January 2024

There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
Sentiment Analysis on Disputed Territory Discrepancies Using Machine Learning-based Text Mining Approach
Authors: Rehman Mustajeeb ur , Bashir Maria
Published: January 2024

Conducting a comparative study of real-time sentiment analysis poses a significant challenge due to the continuous variation in individuals' accents and the difficulty in quantifying them. In computer science, specifically supervised machine learning classifiers, researchers face the obstacle of lacking direct observation. This study explores the performance of well-known supervised machine learning classifiers such as SVM, K-SVM, and Multinomial Naïve Bayes. We utilize a comprehensive corpus of real-time Twitter tweets and news blogs related to disputed territories. These classifiers are trained on the Kaggle dataset for real-time sentiment analysis to achieve the highest accuracy and subsequently tested on real-time data. Notably, the kernel support vector machine performs better in real-time data, as evidenced by the substantial proportion of positive sentiments detected. Furthermore, our study offers a pathway for young scholars to assess the real-time sentiment correlation between corresponding countries, enabling predictions of potential conflicts that may lead to significant casualties
Using Machine Learning Models for The Prediction of Coronary Arteries Disease
Authors: Bilal Muhammad , Aslam Naeem , Naeem Ahmad , Abid Muhammad Kamran
Published: January 2024

Globally, the leading cause of mortality among both men and women is coronary heart disease. This disease is widely recognized as the primary killer worldwide, and its early detection poses a significant challenge. Given the current state of affairs, it is crucial to promptly identify heart disease in its initial stages to ensure successful patient treatment. Despite numerous attempts by various researchers to develop hybrid and ensemble models for early detection, the desired outcomes have not been achieved. Consequently, the machine learning and algorithmic research community has directed its focus towards improving these methodologies. In this particular study, six supervised machine learning classifiers, namely Random_Forest, extreme gradient boost, Logistic of Regression, Decision_Tree, KNN, and N-Bayes, were employed. The UCI repository dataset was utilized as the sample data, comprising attributes and corresponding values. Data preprocessing techniques were employed to eliminate any missing values. An ensemble model incorporating three algorithms, namely DT (decision-tree), RF (random-forest), and XGB, was constructed. Remarkably, the ensemble model achieved an impressive accuracy rate of 95.33% for predicting coronary heart disease.
LCCNet: A Deep Learning Based Method for the Identification of Lungs Cancer using CT Scans
Authors: Khaliq Kiran , Naeem Ahmed , Aslam Naeem , Malik Abdul , Abid Kamran
Published: January 2024

Lung cancer is a highly lethal disease affecting both males and females nowadays. It is essential to identify cancer accurately at the initial stage of lung cancer. However, accurately diagnosing cancer remains a challenging task for pathologists. Among the various techniques available, CT Scan plays a crucial role in the early identification and treatment of lung cancer. For the classification of lung cancer, lots of developing techniques are used in the medical research field. Unfortunately, these techniques achieve less classification accuracy due to poor learning rate, class imbalance, data overfitting, and vanishing gradient. It is essential to develop an accurate, faster, and well-organized system for the classification of lung cancer. To address these issues, an efficient framework called LCCNet is presented, which is transfer learning applied to the pre-trained Densely Connected Convolutional Networks (DenseNet-121) CNN model. LCCNet is used to accurately classify lung cancer. The most common transfer learning and data augmentation approaches are used to deal with a large dataset. LCCNet utilized CT Scans for the accurate classification of lung cancer. To assess the performance, the model utilizes various evaluation metrics such as accuracy, F1-score, precision, and recall along with a confusion matrix to validate the efficiency of the model for lung cancer classification. Furthermore, this study also compares several current studies with the proposed LCCNet model in terms of accuracy measures, showing that the proposed LCCNet model attained the greatest accuracy of 99% when compared to the various existing research fields of study. To the best of our knowledge, the proposed methodology performs efficiently.
Identification of Lungs Cancer by using Watershed Machine Learning Algorithm
Authors: Parveen Razia , Saleem Ujala , Abid Kamran , Aslam Naeem
Published: January 2024

The most dangerous and quickly spreading form of cancer in the world is lung cancer. In terms of fatalities among cancer diseases, it comes in first place, and therapy is made more challenging by late-stage diagnosis. Early identification and detection are crucial for treating this lethal condition, though. Benign and malignant tumors are the two forms that manifest in the early stages of this illness. These are visible with a computed tomography (CT) scan. Thanks to machine learning, these pictures can be used to determine the stages of cancer. In this study, a machine learning framework is presented using the proposed convolutional neural network techniques in order to develop a reliable and precise classification model for the diagnosis of lung cancer and to address the problem of class imbalance datasets, a general problem in medical data that results in difficulties and mistakes in prediction. The data source for the investigation was the IQ-OTHNCCD dataset. Scale Invariant Feature Transform (SIFT) and Watershed were the best feature extraction methods employed in this work, which was provided as a segmentation method. A dataset imbalance is later resolved by data augmentation, and CNN is used to achieve classification. In the malignant lung image, we finally identify the nodule. An accuracy rate of 0.97% is achieved with the proposed CNN-based classification of CT scan pictures.
Deep Emotions Recognition from Facial Expressions using Deep Learning
Authors: Shahzadi Iram , Fuzail Mr. Muhammad , Aslam Dr. Naeem
Published: January 2024

Deep emotion recognition has a wide range of applications, including human-robot communication, business, movies, services hotels, and even politics. Despite the use of various supervised and unsupervised methods in many different fields, there is still a lack of accurate analysis. Therefore, we have taken on this challenge as our research problem. We have proposed a mechanism for efficient and fine-grained classification of human deep emotions that can be applied to many other problems in daily life. This study aims to explore the best-suited algorithm along with optimal parameters to provide a solution for an efficient emotion detection machine learning system. In this study, we aimed to recognize emotions from facial expressions using deep learning techniques and the JAFFE dataset. The performance of three different models, a CNN (Convolutional Neural Network), an ANN (Artificial Neural Network), and an SVM (Support Vector Machine) were evaluated using precision, recall, F1-score, and accuracy as the evaluation metrics. The results of the experiments show that all three models performed well in recognizing emotions from facial expressions. The CNN model achieved a precision of 0.653, recall of 0.561, F1-score of 0.567, and accuracy of 0.62. The ANN model achieved a precision of 0.623, recall of 0.542, F1-score of 0.542, and accuracy of 0.59. The SVM model achieved a precision of 0.643, recall of 0.559, F1-score of 0.545, and accuracy of 0.6. Overall, the results of the study indicate that deep learning techniques can be effectively used for recognizing emotions from facial expressions using the JAFFE dataset.
An empirical study of performance of block cipher algorithms in cloud computing environment
Authors: Jamali Mujeeb-ur-Rehman , Nabi Ghulam , Baloch Abdul Khalique , Baloch Abdul Rehman , Jamali Aadil , Shaikh Riaz Ahmed
Published: January 2024

The security of private and sensitive data stored in the public domain is a major problem. It is critical for the user that data be safe both in transit and even after it has been stored on the server. The data owner must be guaranteed that the data held on the service provider site is safeguarded against data theft from outsiders, and the data must be protected even from the service providers. The secret key generation is one of the most important factors for the security of any cryptographic system because the length of the key directly affects the performance and prevents various cryptographic attacks such as brute force attacks. At the application level, our developed system efficiently secures sensitive, private, and personally identifiable information by ensuring privacy and confidentiality of data at rest in the public domain. This study also compares the performance of block cipher algorithms DES, 3DES, Blowfish, and AES. It was deduced from the result that AES consumes less time when compared to other symmetric algorithms with small consistent behavior for various cryptographic operations with small, medium and big datasets..
LeafNet: Using Convolutional Neural Network for Plant Leaf Detection
Authors: Saeed Saba , Faiz Sana , Meghji Areej Fatemah
Published: January 2024

Pakistan is home to thousands of plant species. As a result of pollution, natural disasters, and climate change, many of these species are at risk of extinction. Plant categorization and detection systems are designed to assist non-experts in automatically identifying plants based on their leaves to ensure their safety. The current study proposes a plant leaf detection system utilizing a Convolutional Neural Network architecture. Making use of the PlantVillage dataset, the proposed system can identify seven species of plants namely apple, cherry, tomato, potato, soybean, strawberry, and corn. Data augmentation strategies have been used to provide more training examples to get around the problem of bias and imbalanced data. The accuracy achieved on the training set was 98.87% which improved to 99.5% when using data augmentation. Apart from the monitoring of endangered species, the adoption of the proposed model can also aid the evaluation of weed management efforts and analysis of species distribution under climate change.
Optimized Classification of Cardiovascular Disease Using Machine Learning Paradigms
Authors: Kanwal Fouzia , Abid Mr. Kamran , Maqbool Muhammad Sajid , Aslam Dr Naeem , Fuzail Muhammad
Published: January 2024

Nearly 19 million people die each year from cardiovascular and chronic respiratory diseases, which are a global threat. It is necessary to address the causes of these diseases because of the high death rate. The investigation uncovered a number of causes, but the inability to forecast these diseases symptoms is by far the most significant. In this work, we developed a method for anticipating these diseases crucial symptoms, which will aid in early disease diagnosis and allow patients to begin treatment. This research will introduce a new computational medicine research using machine learning (ML) paradigms to forecast cardiovascular disease (CVD). Data were processed by methods in sequence with various parameters. different models created that predicts CVD risk based on individual age, gender, ethnicity, body mass etc., and lifestyle factors. The research will also focus on performing complete comparison of ML models. We will apply Five ML based algorithems such as Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBOOST and Random Forest and evaluate these models on the basis of Training and Testing and also calculated the Presicion Recall and F1-Score for each model. Naïve Bayes and XGBOOST Classifier perform better with accuracy of 92.31 and 92.34 percent as compared to other models.
Brain Tumor Segmentation using Deep Learning
Authors: Sajid Muhammad , Yaseen Wajeeha , Khan Aman Ullah
Published: January 2024

In addition to helping doctors discover and measure tumors, it also helps them develop better recovery and treatment plans. Recent MRI brain tumor segmentation algorithms have focused on U-Net design to combine high-level and low-level features for improved accuracy. Fully convolutional networks, which are also used for this purpose, are unable to successfully reconstruct the image through the decoder path because of the insufficient and low-level information from the encoder path. More effort needs to be done to optimise the low-level information flow from the encoder path to the decoder path in order to improve image reconstruction. In this study, we suggested a transfer learning residual U-Net model that combines the U-Net and VGG-16 architectures. To improve image reconstruction, VGG-16 is combined with the encoder. Additionally, a residual path in skipping connection is included to highlight key feature details while muting noisy and unnecessary feature replies.  It is trained using The Cancer Imaging Achieve (TCIA) and Brats 2018 datasets, and It makes it easier to segment small brain tumors. When compared to previous brain tumor segmentation techniques, the suggested model performs competitively.
Azure-Based Multi-Sensor IoT Network for Smart Rice-Nursery Field
Authors: Jhatial Muhammad Juman , Shaikh Dr. Riaz Ahmed , Arain Dr Rafaqat Hussain , Bhutto Khalid Hussain , Talpur Sawan Ali
Published: January 2024

Rice farmers rely on nurseries for various reasons, as they play a pivotal role in cultivating high-quality rice crops. These nurseries provide essential seedlings for subsequent transplantation to the paddy fields. The success of rice cultivation hinges on maintaining a robust and thriving nursery, resulting in improved yields and superior grain quality. To enhance nursery management, a novel method proposes real-time monitoring of environmental conditions. A trial study evaluated the system's performance, demonstrating a significant increase in precision and efficiency, leading to higher crop yields and reduced production costs. This innovative approach has the potential to revolutionize rice nursery practices, promoting sustainability and effectiveness. The study introduces an Internet of Things (IoT)-based real-time monitoring system implemented in a rural area of Sindh, Pakistan. Utilizing a network of sensors, the system gathers vital environmental data that impacts rice nursery growth. The recorded information is then analyzed on the Azure cloud platform, and data visualization is achieved through Power BI. Additionally, an email notification component alerts farmers and agricultural experts based on the sensor data, facilitating timely actions and informed decision-making.
The Design and Evaluation of Novel Ananimated CAPTCHA Schemes Based on Humans’ Natural Vision Capabilities
Authors: Arain Rafaqat Hussain , Shaikh Riaz Ahmed , Shah Safdar Ali , Shah Sajjad Ali , Rafique Saima
Published: January 2024

CAPTCHAs are ubiquitously found on the web these days. It is most commonly used security mechanism against bots. Numerous design variants of static text-based CAPTCHAs were proposed and implemented on the internet over the years. However due to advancements in machine learning and image processing techniques they were proved vulnerable against automated attacks. In this research a new and robust animated CAPTCHA scheme is presented which is based on human’s natural vision capabilities. The newly designed CAPTCHAs are user friendly for humans but extremely hard for bots. The security and usability is kept in mind while designing these CAPTCHAs. Various types of characters, patterns and filters are used to protect them against automated attacks. Overall, 20 types of animated CAPTCHAs are designed in this work. Further in this research, a group of participants have evaluated the usability of newly designed CAPTCHAs and solved with an average success rate of 76.85%. The analysis has proved that the proposed scheme is very much usable for humans and extremely difficult for bots.
A Novel Method for Ranking a Website Rating via Search Engine Optimization
Authors: Raza Muhammad Ahsan , Raza Binish , Ashraf Mahmood , Raza Sehrish
Published: January 2024

Everyone uses the internet and a search engine (SE) to find what they need, making SE indispensable. Search engines are used to find any sort of information, which is why most companies want to rank well in SE result pages in order to reach their ideal clients. Without a website, companies nowadays have little chance of competing in the industry. As a consequence, the companies have prioritized and funded efforts to improve their website SE rankings. There are a plethora of methods that may be used to optimize a website for search engines. These methods come under a heading, namely, search engine optimization (SEO) by webmasters. In this research, we propose a methodology that combines on-page and off-page SEO strategies to improve user search and website ranking over SE. The proposed method outperforms in terms of website ranking, whereby a website ranking is improved from fifth to second position on Google SE.  This significant shift illustrates that on and off page SEO can work together to boost a website visibility over SE result page.
Detecting Monkeypox in humans using deep learning
Authors: Ijaz Muhammad Arslan , Abid Muhammad Kamran , Aslam Naeem , Mudaseer Abdul Qadeer
Published: January 2024

The monkeypox virus is an orthopox virus that causes a contagious illness of the same name. The most visible symptom, along with fever, headache, and muscular pains, is a broad rash that develops into fluid-filled blisters. In the event of a monkeypox outbreak, swift response and efficient public health management depend on an early and accurate diagnosis. In this study, the feasibility of using deep keep learning techniques to diagnose monkeypox in humans is investigated. Long short-term memory (LSTM) networks are used to analyse time-series recordings of symptoms or patient data, whereas convolutional neural networks (CNNs) are used to process medical images of skin lesions. These models need to be trained on a large and reliable data set so that they can identify patterns and attributes that are specific to monkeypox.
Evaluating Accuracy of Pathogenicity Prediction Methods for Single Nucleotide Polymorphisms
Authors: Manzoor Hira , Aslam Naeem , Pervez Muhammad Tariq , Muhammad Syed Shah , Mubashra Ayesha
Published: January 2024

Pathogenicity of single nucleotide polymorphism is the potential ability to produce disease. Testing each of the SNPs separately can lead to an erroneous measurement of the effect of the SNPs on the disease risk. In this research analysis of seven most popular tools for predicting the deleteriousness of single nucleotide polymorphisms namely SIFT, SNPs&GO, I Mutant, MUPro, Fathmn, PANTHER, and PhD-SNP was conducted. The ClinVar database was used to retrieve the pathogenic and benign SNPs, and the UniProt database to get protein sequences respectively. The SIFT, PhD-SNP, and SNP&Go outperformed all of the other prediction algorithms based on accucy and Matthews Correlation Coefficient with scores of (0.68,0.38), (0.66, 0.33) and (0.64, 0.29) respectively with highlighting error rates and recommended to avoid the use of MuPro for predicting the pathogenic variants. To improve the performance and accuracy of pathogenicity predictors the tools must be considered to upgrade.
Predicting the Karachi Stock Price index with an Enhanced multi-layered Sequential Stacked Long-Short-Term Memory Model
Authors: Mahboob Khalid , Shahbaz Muhammad Huzaifa , Ali1 Fayyaz , Qamar Rohail
Published: January 2024

The study proposes the use of a stacked Long-Short-Term Memory (LSTM) model to predict the KSE-100 stock exchange trend and provides a comprehensive review of the literature on deep learning models and time series forecasting in the stock market. The study's findings suggest that the stacked LSTM model outperforms other models in terms of prediction accuracy. The study's contribution lies in its approach to improving the accuracy of stock price prediction using deep learning models. The stacked LSTM model architecture is a novel approach that provides better results than other traditional time series forecasting models. Furthermore, the study's use of hyper-parameter optimization techniques demonstrates the importance of model tuning for improving performance intended for accurate time series forecasting in the financial market. The study's results have practical implications for investors, who can use the stacked LSTM model to make informed decisions about buying or selling stocks in the KSE-100. The model's ability to predict stock prices accurately can help investors maximize their profits and minimize their losses. Hence, the proposed stacked LSTM model can effectively predict stock prices in the KSE-100 and can assist investors in making informed decisions in the stock market.
Identification and Classification of Extremist by Topic Modeling Sentiment Analysis
Authors: Bano Hafsa , Akbar Wasif , Aslam Naeem , Bilal Muhammad
Published: January 2024

Social Media forums were formerly seen as the promised land for amusement, education, and boosting purposes that energize the evolution of text analysis to acknowledge the complications of real world. A seedbed for toxic conduct, fanatical content, and political propaganda, they have now become a sump. YouTube is one of the finest platforms where millions of comments thrown per day which consists of valuable and misleading information. Comments may contain slang, foreign and misspelled words that’s very laborious to perform natural language processing (NLP) with those informal languages. Most preceding research diffused the analysis of gigantic unstructured data that comes in different formats and not simply classify in current databases. This study aims to analyze dynamic textual data from randomly selected channels on YouTube. For this ambition YouTube information API v3 was used to scrape a variety of data from YouTube videos. We investigated a few hot topics that are having mordant remarks about racism, LGBTQ, sub teen life aspect, and women and girls are just some of the areas where bullying is ordinary. In this study we summarize a custom dataset of 50 video’s comments related to rap songs and other social events to perform topic modelling sentiment analysis that dwindle the cost of data labeling and annotating. The main objective of our research is to identify and classify each word with their probability from the dataset by applying model hyper parameters alpha, beta, gamma and examine the topic coherence score to measure the semantic similarity of words from extracted topics. This study’s experimental results reveal that the analysis was able to achieve significant sentiment analysis efficiency at both the document and word levels.
Mobility and Health Monitoring in People with Different Abilities: A Prototype Enhancing Independence: Innovating an IoT-Integrated Wheelchair for
Authors: Bano Zuhra , Qureshi Farwa , Ansar Moomal , Imdad Nimra , Shams Sarmad , Shamim Fahad
Published: January 2024

Wheelchair is an essential tool for people with disabilities, enabling them to move around independently and participate fully in society. They come in different types, such as manual wheelchairs, power wheelchairs, sports wheelchairs, and pediatric wheelchairs among others. Certain types of disabilities such as Monoplegia, Hemiplegia, Paraplegia, and Quadriplegia pose difficulties in using conventional power wheelchairs. To overcome these hurdles and provide ease to differently-abled individuals, an Advance Monitoring and Assistive Wheelchair (AMAW) is proposed in this work. The Prototype includes a voice-controlled system for controlling the movement of a wheelchair, an IoT-based real-time health monitoring system to monitor the vitals of the patient remotely, a fall detection system for detecting falls, a tracking system for position and location, and an alarm system to alert caretaker in case of a fall. The real-time embedded monitoring system allows the monitoring of the user’s vital signs like temperature, pulse rate and oxygen saturation and the assistive part allows the wheelchair to move around electronically either through voice or through mobile application.  With the assistance of various sensors, the data can easily be monitored remotely by the caretaker at regular intervals of the time. The data display on the LCD fitted onto the wheelchair and in the designed mobile application. Furthermore, the whereabouts of the user are sent via the alert system that notifies the caretaker through GSM in case of changes in parameters and if the user has lost the balance. The vitals through the sensors on the prototype has undergone testing on number of individuals with precise outcomes. In comparison to typical joystick-controlled wheelchairs, this project excels in several aspects, such as its ability to stop or turn using voice commands and avoid collisions with people, furniture, fixed objects, and walls. The user friendly AMAW prototype with real-time monitoring, assistance and alert system may serve as a cost-effective solution in maintaining and providing an independent quality life to differently-abled individuals.
Design and Implementation of Brain-Based Home Automation System
Authors: Rafiq Mujahid Mujahid , Noon Serosh Karim , Mannan Abdul , Awan Tehreem , Nisar Noshaba
Published: January 2024

This paper supports the utilization of EEG signals to control a smart home automation system. The study involves calculating the human brain's attention level using EEG data and subsequently employing this information to operate various devices based on the attention value obtained. The process commences with multichannel EEG recordings, which are then processed using MATLAB software. The first channel (FP1) is isolated from the multichannel EEG data, and subsequent steps involve noise and artifact removal through a bandpass filter ranging from 0.3 to 100 Hz. The Alpha and Beta sub-bands of the EEG data are computed, and the Power Spectral Density is derived from the Alpha and Beta waves. By analyzing the intensities of the Alpha and Beta PSD signals, the subject's attention level is computed and categorized. This attention level indicator is then used to control the operation of smart home electrical devices. The study demonstrates the viability and effectiveness of the proposed EEG-based system for controlling domestic appliances, confirming its successful functionality.
Blockchain Technology Performance of Asymmetric Algorithms: An Empirical Study
Authors: Jamali Mujeeb-ur-Rehman , Channa Dr. Najma Nawaz , Jamali Aadil , Jamali Asad Ali , Ali Mazher , Baloch Abdul Khalique
Published: January 2024

Blockchain technology is a transparent, and unchangeable distributed ledger. It has the potential to transform the way to interact with the digital world by allowing to construct a decentralized database that is tamper-proof. Concerns regarding the security of confidential and sensitive data being outsourced are rising. It is possible that service providers may be dishonest since unscrupulous administrators have access to, may alter, and can misuse private and sensitive data. Security precautions are necessary because sensitive data stored on public clouds has to be safeguarded. There is no mechanism to detect data changes, as data are stored in plaintext. Therefore, maintaining privacy and secrecy is impossible. It is important that data must always be kept secure, even after it has been kept on the server. Data stored on the server must be safeguarded against outsider access and, if the insider cannot be trusted, must also be safeguarded against hostile insiders. Asymmetric algorithms are employed to safeguard data during transmission. Asymmetric cryptography is required in modern security systems, and several algorithms have been devised to provide safe and effective encryption and decryption. Asymmetric algorithms are empirically compared in this study. We evaluated each algorithm's performance by taking into account criteria such as key size, memory utilization, and execution time. Our results show that while all algorithms provide safe encryption and decryption, there are significant performance disparities between them. It is determined, in particular, that ECC required the least amount of memory and had the shortest key size. The findings show that ECC's prime and binary fields created pairs of keys faster and with more security than other asymmetric algorithms with smaller bit sizes. On small, medium, and big datasets, ECC had the fastest execution time for plaintext encryption operations. These findings have important implications for the selection and deployment of asymmetric algorithms in various security systems.
Advanced Vehicle Safety: A Prototype Circuit for Accident Prevention and Emergency Response
Authors: Oad Urooj , Shaikh Muhammad Mujtaba , Dahri Safia Amir , Fatima Mariam , Panhwer Fozia , Ahmad Sarfaraz
Published: January 2024

In contemporary society, the pursuit of robust accident prevention, detection, and reporting systems is paramount, particularly within the context of vehicular safety. This study introduces a meticulously designed prototype circuit, adaptable for deployment across diverse vehicle types. This intelligent circuitry endeavors to mitigate accidents and safeguard human lives by employing a multifaceted approach.The system's primary functions encompass the verification of seat belt usage and the assessment of alcohol consumption by the driver. Additionally, it incorporates a vibration sensor to detect accidents promptly. Complementing these features, the integration of GPS and GSM modules facilitates the rapid notification of emergency services, ensuring prompt assistance in the event of an accident.The core of this system is an Arduino microcontroller, orchestrating the interconnected components to process data and trigger actions based on predefined conditions. The circuit's performance has been rigorously tested, initially through simulation in Proteus software, and subsequently via real-world hardware implementation. Comparative analysis of software and hardware results lends insights into the system's functionality and reliability.The overarching objective of this study is to curtail accidents arising from intoxicated driving, unforeseen driver fatigue, and road obstructions. In instances of accidents, the electronic apparatus employed has the capacity to dispatch spontaneous and precise distress signals to law enforcement and medical personnel, thereby expediting casualty recovery and potentially saving lives. This research advances the fusion of technology and safety measures to augment road safety comprehensively.[6] Upadhyay, V., Gupta, S., Chaturvedi, S. and Singh, D. [2020], ‘Integrated accident prevention detectionand response system (iapdrs)’,International journal of engineering and advanced technology9(3), 2086–2089.[7] Vitkar, S. P., Banare, A. and Nadar, J. [2022], ‘Conceptual framework for accident detection and pre-vention’,Journal of Pharmaceutical Negative Resultspp. 7449–7455.[8] Wu, C., Zhang, P., Zhang, Z., Zheng, W., Xu, B., Wang, W., Yu, Z., Dai, X., Zhang, B. and Zang, K. [2023],‘Slip partitioning and crustal deformation patterns in the tianshan orogenic belt derived from gpsmeasurements and their tectonic implications’,Earth-Science Reviewsp. 104362.[9] Xu, X., Hu, X., Zhao, Y., Lü, X. and Aapaoja, A. [2023], ‘Urban short-term traffic speed prediction withcomplicated information fusion on accidents’,Expert Systems with Applicationsp. 119887.
An ensembling approach to predict hepatitis in patients with liver disease using machine learning
Authors: Arif Muhammad , Abbas Mohsin , Shehzad Muhammad Ahmed , Batool Zakia , Rabia Mahwish , Soomro Abdul Majid
Published: January 2024

With a 3.5% mortality rate, liver disease is one of the worst diseases in existence. Pakistan is targeting this major health issue from several perspectives, to improve prevention, diagnosis, and treatment due to having the highest incidence of liver disorders in the world. For liver problem disease, also known as HEP C, Pakistan is now the second most prevalent country in the world. This is due to the rapid progression of HEP C, which can only be stopped by early diagnosis. If not, it progresses to the last stage of HEP C cirrhosis, which has no other treatment options besides liver transplantation. One and only machine learning algorithms like logistic regression, random forest, KNN, K-Means, and XGBoost can be used to predict liver illness utilizing modern methods like artificial intelligence. Data is gathered from Kaggle and subjected to several machine learning algorithms after pre-processing in order to quickly diagnose liver disease. Additionally, to improve accuracy, all of these algorithms are ensemble, and accuracy is 78.96%, along with precision, recall, and F1 score. In this work, liver disease is predicted early on using pre-processing, feature extraction, and classification techniques. Recall, precision, and f1score metrics are used to compare the accuracy of the six algorithms, and these algorithms are then combined to provide the most accurate diagnosis of liver disease.