Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

The proliferation of digital communication and social media platforms has led to an upsurge in spam messages across various languages, including Bangla. These intrusive messages not only cause user annoyance but also elevate the risk of important messages going unnoticed. To address this concern, we harnessed the power of natural language processing (NLP) techniques in conjunction with machine-learning models. This study delves into the efficacy of diverse machine-learning and deep-learning models in detecting spam messages in the Bangla language. Particularly, deep-learning methodologies such as ELMo and BERT have exhibited promising results in spam detection across other languages. However, the realm of Bangla spam detection remains underexplored, with a dearth of deep-learning approaches. To bridge this gap, we constructed a unique Bangla SMS dataset, given the scarcity of available Bangla Spam SMS datasets, and proceeded to evaluate the performance of ELMo, BERT, and conventional machine learning models. The findings from our experiments underscore the superior performance of deep learning-based models over traditional methods, with Elmo surpassing BERT in Bangla spam detection, achieving an impressive accuracy of approximately 94%. This endeavor bears the potential to not only enhance SMS utilization efficiency but also bolster cybersecurity measures against phishing attacks.

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

Network security is increasingly challenged by sophisticated and evolving cyber threats, underscoring the need for efficient and robust intrusion detection systems (IDS). Although researchers have extensively studied intrusion detection, they have made limited progress in effectively detecting zero-day attacks, particularly those caused by subtle variations. Existing approaches often tackle class imbalance and feature selection independently, with minimal exploration of their combined impact as a unified preprocessing strategy. Moreover, researchers have shown that inconsistencies and artifacts in the widely used CICIDS2017 dataset impair model generalization and stability. This study proposes a systematic pipeline that employs a unified framework, integrating hybrid data balancing with advanced feature selection to enhance anomaly detection performance. By leveraging Extra Trees-based feature selection alongside a strategic combination of SMOTE and targeted undersampling, the approach effectively addresses critical challenges such as class imbalance and feature redundancy. Evaluated on the CICIDS2017 dataset, the framework demonstrates strong generalization across both machine learning and deep learning models. Notably, XGBoost and Decision Tree achieved balanced accuracy and F1-scores of up to 99.99% and 99.96%, respectively, in multiclass classification. The evaluation framework reports comprehensive metrics beyond accuracy, including Precision, Recall, F1-score, Matthews Correlation Coefficient (MCC), and ROC-AUC, ensuring robust assessment under class imbalance conditions. These results highlight the effectiveness of the approach for real-time anomaly detection. The study contributes a robust and efficient pipeline for improving the accuracy and reliability of anomaly-based intrusion detection systems.

Obscenity detection from images and videos are now crucial due to social and ethical reasons. It has been two decades the research on this field started. Most of the works are based on skin color detection, which are not suitable for finding obscenity. The reason for this is that, there are many skins like objects such as beach photos, human skin like animal’s fur, skin colored painting that enables false positive and negative rate. In addition all works performed well on some particular set of images or video data. In this research some aspects of obscenity detection is described delineating strength, weakness and possible extensions of prior works. Introducing some new features and incorporation of multiple classifiers and transfer learning will lead the work more robust. Moreover, traditional multimedia cloud computing has been investigated in this study and proposed some new research ideas.

Early detection of anxiety disorders in a non-intrusive manner is crucial, as these conditions can profoundly impact an individual’s health and daily functioning. Traditional approaches relying solely on unimodal data often fall short, potentially introducing bias and inaccuracies. TI-Fusion is a novel late multimodal fusion technique that integrates text and image data for a unified reliable outcome, overcoming limitations in existing methods. The primary advantage of TI-Fusion is its non-intrusive nature, ensuring patient comfort by avoiding invasive methods while still delivering robust diagnostic capabilities. The study utilizes six advanced machine learning algorithms (Gaussian Naive Bayes, XGB Classifier, K-Neighbors, SVM, Decision-Tree, and RandomForest) for data classification, pattern recognition, and predictive accuracy. Concurrently, image data from the KDEF and CK+ datasets was processed through a Convolutional Neural Network (CNN) enhanced with a Real Gabor filter, which is particularly adept at capturing textures, edges, and complex visual patterns necessary for precise image analysis and recognition. By employing a late multimodal fusion approach, TI-Fusion integrates the outcomes of models trained on distinct data modalities, yielding a more comprehensive and accurate prediction than unimodal methods. This technique not only surpasses existing multimodal approaches but also achieves a commendable final accuracy rate of 92.38%, demonstrating its effectiveness in enhancing the early detection of anxiety disorders.