Mohammad Abrar
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View article: Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images
Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images Open
Precise segmentation of brain tumors is essential for efficient diagnosis and therapy planning. While current automated methods frequently fail to capture complicated tumor shapes, traditional manual methods are laborious, subjective, and …
View article: Correction: Ullah et al. Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures. Mathematics 2023, 11, 4189
Correction: Ullah et al. Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures. Mathematics 2023, 11, 4189 Open
The author’s contribution is incomplete in the original publication [...]
View article: Federated Defense: A Privacy-Preserving Deep Learning Model for IoT Malware Detection
Federated Defense: A Privacy-Preserving Deep Learning Model for IoT Malware Detection Open
As the Internet of Things (IoT) continues to expand, securing the vast network of IoT devices, particularly in Machine-to-Machine (M2M) communication, has become a critical concern. Traditional security approaches often fall short, particu…
View article: EXPLORATORY DATA ANALYSIS AND VISUALIZATION
EXPLORATORY DATA ANALYSIS AND VISUALIZATION Open
Exploratory Data Analysis (EDA) is a foundational step in the data science process, enabling practitioners to uncover meaningful patterns, detect anomalies, and inform subsequent modeling decisions [1, 2]. By systematically visualizing and…
View article: MACHINE LEARNING FUNDAMENTALS FOR DATA SCIENCE: ALGORITHMS, EVALUATION, AND APPLICATIONS
MACHINE LEARNING FUNDAMENTALS FOR DATA SCIENCE: ALGORITHMS, EVALUATION, AND APPLICATIONS Open
This chapter introduces ML fundamentals, focusing on two primary paradigms: supervised learning, where models predict labeled outcomes (e.g., classification, regression), and unsupervised learning, which identifies hidden structures in unl…
View article: STATISTICS AND PROBABILITY FOR DATA SCIENCE
STATISTICS AND PROBABILITY FOR DATA SCIENCE Open
This chapter provides a comprehensive overview of foundational statistical concepts essential for data science. The initial part of statistics focuses on descriptive statistics which defines data description using mean and median and mode …
View article: DATA WRANGLING AND PREPROCESSING FOR DATA SCIENCE
DATA WRANGLING AND PREPROCESSING FOR DATA SCIENCE Open
Data preprocessing forms the critical foundation of effective data science workflows, transforming raw, unstructured data into reliable inputs for analysis and modeling. This chapter emphasizes the pivotal role of preprocessing in addressi…
View article: PROGRAMMING FOR DATA SCIENCE: PYTHON, R, SQL, AND NOSQL
PROGRAMMING FOR DATA SCIENCE: PYTHON, R, SQL, AND NOSQL Open
Programming is the backbone of modern data science, enabling practitioners to manipulate, analyze, and extract insights from vast and complex datasets. This chapter explores the essential roles of Python, R, SQL, and NoSQL technologies in …
View article: A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing
A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing Open
Object detection in remote sensing imagery presents challenges due to low resolution, complex backgrounds, occlusions, and scale variations, which are critical in disaster response, environmental monitoring, and surveillance. This study pr…
View article: Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis
Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis Open
Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face …
View article: ATAD-Net: An Adaptive Deep Learning Framework for Real-Time Financial Fraud Detection
ATAD-Net: An Adaptive Deep Learning Framework for Real-Time Financial Fraud Detection Open
With the fast growth of financial transaction fraud, there is a need for advanced detection systems capable of real-time analysis. Rule-based and machine-learning approaches to fraud traditionally suffer from being unable to adapt to chang…
View article: Dual‐View Deep Learning Model for Accurate Breast Cancer Detection in Mammograms
Dual‐View Deep Learning Model for Accurate Breast Cancer Detection in Mammograms Open
Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogra…
View article: Transformative Transfer Learning for MRI Brain Tumor Precision: Innovative Insights
Transformative Transfer Learning for MRI Brain Tumor Precision: Innovative Insights Open
Accurate brain tumor detection is essential for effective treatment and improved patient outcomes, yet conventional MRI classification methods often struggle with complex and variable imaging data. This study introduces the Transformative …
View article: <scp> m <sup>5</sup> C </scp> ‐ <scp>TNKmer</scp> : Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques
<span> m <sup>5</sup> C </span> ‐ <span>TNKmer</span> : Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques Open
5‐Methylcytosine (m 5 C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m 5 C in deoxyribonucleic …
View article: Advanced neural network-based model for predicting court decisions on child custody
Advanced neural network-based model for predicting court decisions on child custody Open
Predicting court rulings has gained attention over the past years. The court rulings are among the most important documents in all legal systems, profoundly impacting the lives of the children in case of divorce or separation. It is eviden…
View article: m 5 C-TNKmer: Identification of 5-methylated base Cytosine of Ribonucleic Acid using Supervised Machine Learning Techniques
m 5 C-TNKmer: Identification of 5-methylated base Cytosine of Ribonucleic Acid using Supervised Machine Learning Techniques Open
5-methylcytosine (m 5C) is a widely known epigenetic moderation in RNA types. Methyltransferases catalyze the genesis of m5C. This site of RNA plays a crucial role in many biological activities. For many years in DNA, the synthetic process…
View article: An effective deep learning-based approach for splice site identification in gene expression
An effective deep learning-based approach for splice site identification in gene expression Open
A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding intron…
View article: DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome
DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome Open
Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various…
View article: Revolutionizing Brain Tumor Segmentation in MRI with Dynamic Fusion of Handcrafted Features and Global Pathway-based Deep Learning
Revolutionizing Brain Tumor Segmentation in MRI with Dynamic Fusion of Handcrafted Features and Global Pathway-based Deep Learning Open
Gliomas are the most common malignant brain tumor and cause the most deaths.Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience.Manual brain tumor segmentat…