Sadaqat Ali Ramay’s scientific contributions

In today's fast-paced digital economy, businesses increasingly rely on Cloud Transformation, Cyber Security Integration, and 5G Adoption to enhance Business Network Performance and maintain competitive advantage. This study examines how these three factors contribute to business efficiency, security, and digital scalability across enterprises in North America, Europe, and the Middle East. A quantitative research methodology was employed; utilizing a structured questionnaire distributed to 300 businesses and IT

Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main objective of this work is to present a deep machine learning-based approach that provides better results in terms of accuracy. These findings have significance for tailored healthcare 5.0, enabling healthcare professionals to predict chronic disease more efficiently. A comparative examination of the most recent methods has been provided in our work reveals that it might be more advantageous to use the proposed model in which segmentation of the MRI is performed using U-net architecture and then classification is done using transfer learning for chronic disease prediction. Our proposed model provides 96.06% accuracy, it advances our understanding of deep machine learning’s potential for chronic disease prediction and emphasizes the need to tailor model selection to specific disease types using data from IoMT enabled devices. In order to make advanced improvement in the field of healthcare 5.0, future studies should focus on refining these models and investigating how well they work with a wider range of datasets.

Android, with a global mobile operating system market share of 71.17%, has become a primary target for malware attacks, leading to significant social issues such as privacy violations, financial losses, and psychological stress. This study provides insights into the global impact of Android malware, including country-specific attack statistics. While traditional machine learning algorithms have been extensively used for malware detection, their limitations in addressing the evolving complexity of Android malware emphasize the need for deep learning approaches. This research discusses permission-based detection methods and explores alternative models, evaluating their performance across diverse datasets. To address these challenges, a hybrid model is proposed, combining XGBoost for feature enhancement with Convolutional Neural Networks (CNNs) 1 for hierarchical learning. Implemented within a K-Fold cross-validation framework, the model achieves exceptional results, including an average accuracy of 94.23%, precision of 95.75%, recall of 92.41%, F1 score of 93.98%, and ROC AUC of 97.59%. A comparative analysis highlights the model's superiority over traditional machine learning algorithms such as Logistic Regression, Random Forest, Naive Bayes, and KNN in all key performance metrics. The findings demonstrate the potential of integrating feature enrichment with deep learning to develop robust and scalable solutions for Android malware detection.

Thalassemia is a hereditary condition where the body is unable to manufacture enough hemoglobin. Made up of alpha and beta globin proteins, hemoglobin is the most important component of red blood cells (RCB) that delivers oxygen throughout the body. Alpha and beta-globin genes are either rare or nonexistent, which results in alpha and beta-thalassemia. Beta thalassemia is more dangerous because of the increase in the probability of conceiving a kid with thalassemia than the alpha one. Most forms of thalassemia cause chronic and lifelong anemia that exists in early childhood and requires a blood transfusion due to deformity of blood cells frequently throughout the patient's life. The body makes glucose as a result of the oxygen carried by red blood cells, which enables normal body function. Thus, thalassemia impacts the body's ability to distribute oxygen to all of its cells, which can have an impact on organs with severity and even cause death. According to the research anemia caused affects 42% of women worldwide, including 52% of pregnant women in developing nations, compared to 23% in developed economies. In this study, machine learning and statistical analysis are used to forecast and assess the behavior of thalassemia. Moreover, the person with thalassemia should be referred to proper genetic counseling. The person with the alpha thalassemia trait has a normal life expectancy. People with beta-thalassemia often die by the age of 30. The statistical analysis applied in our research are the Independent Samples t-test for Age, the Paired Samples t-test for Hemoglobin (HGB) Levels, Analysis of Variance (ANOVA) for Mean Corpuscular Volume (MCV) Levels Across Age Groups, and the Comparison of Two Hypotheses with Different Means. Moreover, we also investigate the correlation between Red Blood Cells and Hemoglobin. As for the machine learning approaches, we applied supervised machine learning models, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).

Abstract: Skin cancer, characterized as a chronic disease, demands time-consuming and costly medical tests for accurate detection, thereby introducing risks associated with treatment delays. Acknowledging the critical need for efficient skin cancer detection, this thesis endeavors to make a significant contribution by proposing an advanced deep learning methodology. The innovative approach involves enhancing the ResNet model with SE modules and integrating a maximum pooling layer within the ResBlock shortcut connection. In comparison to established models (ResNet-50, SENet, DenseNet, and GoogleNet), the proposed method surpasses them in accuracy, parameter efficiency, and computation speed, achieving an impressive average recognition accuracy of 97.48% on a comprehensive 2142-image dataset. This transformative solution aspires to not only revolutionize skin cancer detection but also elevate the standard of patient care in this critical domain.

The most recent statistics show that of all cancers, cancer of the breast is the most common, killing about 900,000 individuals annually. Finding the disease early and correctly diagnosing it can increase the chances of a good result, which lowers the death rate. Early diagnosis can, in fact, prevent the disease from spreading and prevent premature victims from experiencing it. In this work, a comparison is made between advanced deep learning techniques and traditional machine learning for the analysis of breast cancer. We evaluated a deep learning model based on neural networks and traditional machine learning approaches such as Support Vector Classifier (SVC), Decision Tree, and Random Forest. Several demographic and clinical data were included in the diverse dataset of this investigation. This study compared traditional machine learning models (Random Forest, Decision Tree, SVC) with a neural network-based deep learning model in breast cancer analysis using features such as age, family history, genetic mutation, hormone therapy, mammogram results, breast pain, menopausal status, BMI, alcohol consumption, physical activity, smoking status, breast cancer diagnosis, frequency of screening, awareness source, symptom awareness, screening preference, and geographical location. SVC obtained an 86.36%, Decision Tree an 86.18%, and Random Forest an 86.00%. The deep learning model more precisely, a neural network outperformed these results with a highest 93% accuracy. To evaluate their diagnostic usefulness for breast cancer analysis, this study compares deep learning algorithms with more traditional machine learning methods. Accuracy ratings for the machine learning models were 86.00% for Random Forest, 86.18% for Decision Tree, and 88.36% for Support Vector Classifier.

The study is centered around identifying Android malware using deep learning methods through Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). With Android being widely used worldwide ensuring the security of released applications poses a challenge. Conventional malware detection techniques, like dynamic analysis have limitations in recognizing new malware types leading to a shift towards machine learning and deep learning solutions. The research introduces a malware detection system that employs GNNs particularly focusing on GCNs to analyze the relationships within an applications code by transforming APK files into graph formats. The system follows stages including data gathering, feature extraction, graph construction, model training and implementation. By concentrating on function call graphs the system proves effective in identifying software surpassing traditional machine learning methods in terms of accuracy, precision, recall and F1 score. The GCN based model shows enhancements over approaches with an accuracy rate of 95% compared to 89%, for traditional machine learning models. This progress highlights the potential of learning techniques in bolstering Android security. The system excels not in identifying software but also proves versatile, for different uses like screening apps, in stores and functioning as a standalone antivirus program.

The exponential expansion of Cloud computing has resulted in a solution revolution within data storage and employ, but it has also increased security concerns. Typically, traditional cryptographic methods fail to find the right balance between security strength and performance efficiency in resource-poor cloud environment. This study, therefore, focuses on the emerging field of effective and novel cryptographic algorithms that have been developed to strengthen cloud security. We investigate recent innovations in areas such as homomorphic encryption, attribute-based encryption, lightweight cryptography and quantum resistant cryptography. Each method is analyzed from the perspective of security, performance, applicability and individual advantages as well as flaws. Comparing and contrasting these approaches, we elucidate their potential for meeting important cloud security challenges including data privacy; access control, secure computation. We also discuss some of the remaining open questions and future research directions as we strive to produce stronger, more effective cryptographic solutions for what is likely to be an ever-changing cloud paradigm. This survey is intended as a one stop shop for all researchers and practitioners by taking them through the dynamic world of cryptography setting against cloud security.

This study proposes a novel methodology for classifying ocular diseases using convolutional neural networks (CNNs) and specialized loss functions. The proposed model architecture incorporates a convolutional layer, global average pooling, ReLU activation, and novel loss functions (FL and CILF) to improve classification performance. The CNN architecture consists of three main layers: the convolutional layer (ConvL), global average pooling layer (GAPL), and fully connected layer (FCL). Trained on RFCI images with dimensions 299 x 299 x 3, the model effectively captures low-level features such as edges and curves, enhancing visual recognition capabilities. Convolutional operations are applied systematically across the entire image, with filters learning weights during training to extract relevant features. Experimental evaluation is conducted using two publicly available Ocular Health Dataset (OHD) datasets, comparing the proposed model with established baseline models (DenseNet-169, EfficientNet-B7, ResNet-101, Inception-V3, and VGG-19). Additionally, an ablation study is performed to assess the effectiveness of the proposed model. Results, averaged over three cross-validation tests, demonstrate the model's efficacy in classifying ocular diseases, particularly for categories such as CATR, AMD, and GLU.