Shahran Rahman Alve

Samiha Islam, Muhammad Zawad Mahmud, Shahran Rahman Alve et al.

The goal of the present study is to analyze an application of deep learning models in order to augment the diagnostic performance of oral squamous cell carcinoma (OSCC) with a longitudinal cohort study using the Histopathological Imaging Database for oral cancer analysis. The dataset consisted of 5192 images (2435 Normal and 2511 OSCC), which were allocated between training, testing, and validation sets with an estimated ratio repartition of about 52% for the OSCC group, and still, our performance measure was validated on a combination set that contains almost equal number of sample in this use case as entire database have been divided into half using stratified splitting technique based again near binary proportion but total distribution was around even. We selected four deep-learning architectures for evaluation in the present study: ResNet101, DenseNet121, VGG16, and EfficientnetB3. EfficientNetB3 was found to be the best, with an accuracy of 98.33% and F1 score (0.9844), and it took remarkably less computing power in comparison with other models. The subsequent one was DenseNet121, with 90.24% accuracy and an F1 score of 90.45%. Moreover, we employed the Local Interpretable Model-agnostic Explanations (LIME) method to clarify why EfficientNetB3 made certain decisions with its predictions to improve the explainability and trustworthiness of results. This work provides evidence for the possible superior diagnosis in OSCC activated from the EfficientNetB3 model with the explanation of AI techniques such as LIME and paves an important groundwork to build on towards clinical usage.

Muhammad Zawad Mahmud, Samiha Islam, Shahran Rahman Alve et al.

An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used to solve. The biological system known as IoT has seen a rapid increase in high dimensionality and information traffic. Self-protective mechanisms like intrusion detection systems (IDSs) are essential for defending against a variety of attacks. On the other hand, the functional and physical diversity of IoT IDS systems causes significant issues. These attributes make it troublesome and unrealistic to completely use all IoT elements and properties for IDS self-security. For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy (our strategy). A five-ML algorithm model-based IDS for machine learning-based networks with proper hyperparamater tuning is presented in this paper by examining how the most popular feature selection methods and classifiers are combined, such as K-Nearest Neighbors (KNN) Classifier, Decision Tree (DT) Classifier, Random Forest (RF) Classifier, Gradient Boosting Classifier, and Ada Boost Classifier. The Random Forest (RF) classifier had the highest accuracy of 99.39%. The K-Nearest Neighbor (KNN) classifier exhibited the lowest performance among the evaluated models, achieving an accuracy of 94.84%. This study's models have a significantly higher performance rate than those used in previous studies, indicating that they are more reliable.

Shahran Rahman Alve, Muhammad Zawad Mahmud, Samiha Islam et al.

Artificial intelligence and deep learning are increasingly applied in the clinical domain, particularly for early and accurate disease detection using medical imaging and sound. Due to limited trained personnel, there is a growing demand for automated tools to support clinicians in managing rising patient loads. Respiratory diseases such as cancer and diabetes remain major global health concerns requiring timely diagnosis and intervention. Auscultation of lung sounds, combined with chest X-rays, is an established diagnostic method for respiratory illness. This study presents a Deep Convolutional Neural Network (CNN)-based approach for the analysis of respiratory sound data to detect Chronic Obstructive Pulmonary Disease (COPD). Acoustic features extracted with the Librosa library, including Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Spectrogram, Chroma, Chroma (Constant Q), and Chroma CENS, were used in training. The system also classifies disease severity as mild, moderate, or severe. Evaluation on the ICBHI database achieved 96% accuracy using 10-fold cross-validation and 90% accuracy without cross-validation. The proposed network outperforms existing methods, demonstrating potential as a practical tool for clinical deployment.

Muhammad Zawad Mahmud, Shahran Rahman Alve, Samiha Islam et al.

Software-defined network (SDN) is a new approach that allows network control to become directly programmable, and the underlying infrastructure can be abstracted from applications and network services. Control plane). When it comes to security, the centralization that this demands is ripe for a variety of cyber threats that are not typically seen in other network architectures. The authors in this research developed a novel machine-learning method to capture infections in networks. We applied the classifier to the UNSW-NB 15 intrusion detection benchmark and trained a model with this data. Random Forest and Decision Tree are classifiers used to assess with Gradient Boosting and AdaBoost. Out of these best-performing models was Gradient Boosting with an accuracy, recall, and F1 score of 99.87%,100%, and 99.85%, respectively, which makes it reliable in the detection of intrusions for SDN networks. The second best-performing classifier was also a Random Forest with 99.38% of accuracy, followed by Ada Boost and Decision Tree. The research shows that the reason that Gradient Boosting is so effective in this task is that it combines weak learners and creates a strong ensemble model that can predict if traffic belongs to a normal or malicious one with high accuracy. This paper indicates that the GBDT-IDS model is able to improve network security significantly and has better features in terms of both real-time detection accuracy and low false positive rates. In future work, we will integrate this model into live SDN space to observe its application and scalability. This research serves as an initial base on which one can make further strides forward to enhance security in SDN using ML techniques and have more secure, resilient networks.

Farhana Elias, Md Shihab Reza, Muhammad Zawad Mahmud et al.

The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable artificial intelligence (XAI) to improve model transparency. Osteoporosis is a significant public health concern, sometimes remaining untreated owing to its asymptomatic characteristics, and early identification is essential to avert fractures. The research assesses six machine learning classifiers: Random Forest, Logistic Regression, XGBoost, AdaBoost, LightGBM, and Gradient Boosting and utilizes a dataset based on clinical, demographic, and lifestyle variables. The models are refined using GridSearchCV to calibrate hyperparameters, with the objective of enhancing predictive efficacy. XGBoost had the greatest accuracy (91%) among the evaluated models, surpassing others in precision (0.92), recall (0.91), and F1-score (0.90). The research further integrates XAI approaches, such as SHAP, LIME, and Permutation Feature Importance, to elucidate the decision-making process of the optimal model. The study indicates that age is the primary determinant in forecasting osteoporosis risk, followed by hormonal alterations and familial history. These results corroborate clinical knowledge and affirm the models' therapeutic significance. The research underscores the significance of explainability in machine learning models for healthcare applications, guaranteeing that physicians can rely on the system's predictions. The report ultimately proposes directions for further research, such as validation across varied populations and the integration of supplementary biomarkers for enhanced predictive accuracy.

Shahran Rahman Alve, Muhammad Zawad Mahmud, Samiha Islam et al.

The Internet of Things (IoT) is expanding at an accelerated pace, making it critical to have secure networks to mitigate a variety of cyber threats. This study addresses the limitation of multi-class attack detection of IoT devices and presents new machine learning-based lightweight ensemble methods that exploit its strong machine learning framework. We used a dataset entitled CICIoT 2023, which has a total of 34 different attack types categorized into 10 categories, and methodically assessed the performance of a substantial array of current machine learning techniques in our goal to identify the best-performing algorithmic choice for IoT application protection. In this work, we focus on ML classifier-based methods to address the biocharges presented by the difficult and heterogeneous properties of the attack vectors in IoT ecosystems. The best-performing method was the Decision Tree, achieving 99.56% accuracy and 99.62% F1, indicating this model is capable of detecting threats accurately and reliably. The Random Forest model also performed nearly as well, with an accuracy of 98.22% and an F1 score of 98.24%, indicating that ML methods excel in a scenario of high-dimensional data. These findings emphasize the promise of integrating ML classifiers into the protective defenses of IoT devices and provide motivations for pursuing subsequent studies towards scalable, keystroke-based attack detection frameworks. We think that our approach offers a new avenue for constructing complex machine learning algorithms for low-resource IoT devices that strike a balance between accuracy requirements and time efficiency. In summary, these contributions expand and enhance the knowledge of the current IoT security literature, establishing a solid baseline and framework for smart, adaptive security to be used in IoT environments.

Shahran Rahman Alve

This project aims to develop a robust video surveillance system, which can segment videos into smaller clips based on the detection of activities. It uses CCTV footage, for example, to record only major events-like the appearance of a person or a thief-so that storage is optimized and digital searches are easier. It utilizes the latest techniques in object detection and tracking, including Convolutional Neural Networks (CNNs) like YOLO, SSD, and Faster R-CNN, as well as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), to achieve high accuracy in detection and capture temporal dependencies. The approach incorporates adaptive background modeling through Gaussian Mixture Models (GMM) and optical flow methods like Lucas-Kanade to detect motions. Multi-scale and contextual analysis are used to improve detection across different object sizes and environments. A hybrid motion segmentation strategy combines statistical and deep learning models to manage complex movements, while optimizations for real-time processing ensure efficient computation. Tracking methods, such as Kalman Filters and Siamese networks, are employed to maintain smooth tracking even in cases of occlusion. Detection is improved on various-sized objects for multiple scenarios by multi-scale and contextual analysis. Results demonstrate high precision and recall in detecting and tracking objects, with significant improvements in processing times and accuracy due to real-time optimizations and illumination-invariant features. The impact of this research lies in its potential to transform video surveillance, reducing storage requirements and enhancing security through reliable and efficient object detection and tracking.

Faria Naznin, Md Touhidur Rahman, Shahran Rahman Alve

A significant challenge in automating hate speech detection on social media is distinguishing hate speech from regular and offensive language. These identify an essential category of content that web filters seek to remove. Only automated methods can manage this volume of daily data. To solve this problem, the community of Natural Language Processing is currently investigating different ways of hate speech detection. In addition to those, previous approaches (e.g., Convolutional Neural Networks, multi-channel BERT models, and lexical detection) have always achieved low precision without carefully treating other related tasks like sentiment analysis and emotion classification. They still like to group all messages with specific words in them as hate speech simply because those terms often appear alongside hateful rhetoric. In this research, our paper presented the hate speech text classification system model drawn upon deep learning and machine learning. In this paper, we propose a new multitask model integrated with shared emotional representations to detect hate speech across the English language. The Transformer-based model we used from Hugging Face and sentiment analysis helped us prevent false positives. Conclusion. We conclude that utilizing sentiment analysis and a Transformer-based trained model considerably improves hate speech detection across multiple datasets.