Heart disease detection at its early stages remains vital because it leads to lower death rates alongside better healthcare results for patients. This research develops an advanced model CardioSense-Net which detects cardiac diseases through machine learning methods by combining multiple types of medical data. The methodology follows five essential phases to complete its tasks. The system acquires data from public databases together with hospital records which merge ECG and echocardiography analysis and patient medical reports. The data becomes more robust through implementations of Z-score normalization together with data augmentation and missing value imputation. The combination of Wavelet Transform and Higher-Order Spectral Analysis extracts three categories of features including temporal features and morphological features and statistical features. A combination of Principal Component Analysis (PCA) and Mutual Information-based Feature Selection (MIFS) method removes redundant features from the dataset. The proposed algorithm CardioSense-Net applies Convolutional Neural Network (CNN) together with Bi-directional Long Short-Term Memory (BiLSTM) for Deep Learning (DL) evaluation which combines spatial and temporal dependencies present in ECG signals alongside echocardiograms. Through CNN spatial features get extracted simultaneously BiLSTM learns the vital sequence dependencies for improved disease classification results. A fine-tuning process of hyperparameters uses the improved Grey Wolf Optimization (IGWO) algorithm to optimize the proposed model. The model reaches 98.3% accuracy when validating it on benchmark datasets and surpasses all state-of-the-art methods currently available. Performance evaluations of the model use precision together with recall while F1-score and AUC-ROC measurements also provide assessment results.
Cloud Computing has fundamentally transformed how organizations worldwide provision and manage infrastructure, while at the same time introducing notable challenges for digital forensics and incident response. The traditional forensic approach relies heavily on physical access and static storage. This approach does not work well with cloud environments, as everything is virtual and dynamic, and evidence is distributed across many services and is volatile in nature. This paper presents a flexible cloud-focused forensic analysis framework. The framework integrates data from various AWS services, including CloudTrail, VPC Flow Logs, AWS Config, IAM Activity, and Security Configurations. It also gathers data from virtual machines, such as system logs and live memory snapshots, all managed by an API-driven framework. This framework helps investigators connect different types of evidence and build clear timelines, making it easier to identify security problems with less manual work. We tested this framework in a typical AWS environment. The results indicate that it can collect and link evidence from different parts of a system and assist in reconstructing events during suspicious activities. This work suggests that the framework can improve forensic processes and points to future research in automated forensics for both single-cloud and multi-cloud environments.
This paper gives a complete evaluation of Rhythm-Net, an innovative deep learning (DL) technique for the diagnosis of heart disease from multimodal information (ECG, PPG, and clinical information). The model’s performance is tested using standard metrics like accuracy, precision, recall, F1-score, ROC-AUC, and blockchain-based metrics like latency and throughput. Comparative evaluation is performed against both conventional machine learning (ML) models (Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN)), state-of-the-art DL (benchmark) models (Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM) and advanced existing models (DeepHeart, HeartNet, MultiResCNN, ResNet-1D) by comprehensive tabular and graphical results. An ablation study also examines the effect of multimodal fusion, privacy-preserving blockchain incorporation, and important architectural elements such as attention and residual connections. The results show the better diagnostic performance of Rhythm-Net, with high robustness and privacy preservation, and a balanced presentation of advantages, limitations, and clinical readiness.
Text-to-sign language translation remains a challenging task due to structural differences between spoken or written languages and sign languages. Recent research has shifted from rule-based approaches to neural network architectures. It has significantly improved text to gloss translation. This study has four main objectives. The first objective is to conduct a comprehensive literature review to identify widely used neural models for this task and to define the proposed model. The second objective is to design an algorithmic framework for the proposed model. The third objective is to evaluate the proposed model against baseline models and state-of-the-art methods. These experiments are conducted on the benchmark ASLG-PC12 dataset. The results of Research Study 1 demonstrate that the proposed GlossTransformer model consistently outperforms baseline models. It achieves a training accuracy of 99.45% and a testing accuracy of 99.20% where 80:20 split is used for training and testing split. An 80:10:10 train–validation–test split is used in Research Study 2. The results of Research Study 2 are assessed using token-level precision, recall, F1 score and sequence-generation metrics show that the proposed model achieves competitive performance. On the test split, GlossTransformer reports 99.39% accuracy, 96.83% precision, 97.50% recall, 97.09% F1 score, BLEU-1 97.49, BLEU-2 96.55, BLEU-3 95.62, BLEU-4 94.70, WER 2.55, and ROUGE-L 97.51 and perform comparably to existing state-of-the-art methods. Future work is needed to focus on broader testing with other datasets and improved generalization analysis. This framework can later include sign language output and speech recognition to better support people with hearing disabilities.
Speaker diarization determines who talked when by segmenting a recording according to speaker identity and labeling each segment uniformly throughout the entire broadcast. The task has undergone a nearly complete transformation since 2019. The earlier pipeline methods were unable to resolve speech in which voices overlap because they performed voice activity detection, feature extraction, and clustering separately. End-to-end neural diarization (EEND), on the other hand, reframes the task as a frame-level permutation-invariant multi-label classification problem. Instead of treating speaker assignment, overlap detection, and segmentation as distinct subtasks, this unified approach allows a single model to jointly learn each of these tasks. Instead of providing a comprehensive survey, this review, which covers the years 2019–2026, provides an in-depth perspective. In addition to examining recent integrations with foundation models, such as DiariZen, Sortformer, EEND-M2F, and SpeakerLM, it tracks the evolution from typical clustering-based pipelines to self-attention-based EEND architectures, encoder–decoder attractor models, multi-scale decoders, and streaming approaches. The lowest reported diarization error rate (DER) has dropped from roughly 26.2% (VBx, re-evaluated in 2024) to 15.1% (DiariZen, 2025) when assessed under a consistent no-collar approach on the AMI single-distant-microphone (SDM) corpus. In order to provide a harmonized comparison of DER values brought onto a standard evaluation footing under a stated approach, we assess reported performance across eight benchmark datasets. We expand the analysis to language diarization (LD) in code-switched and multilingual contexts, finding fundamental issues with SD that are addressed independently in previous surveys. Overlapping speech at three or more simultaneous speakers, scalable speaker counting, real-time deployment, annotation dependency, and domain generalization are among the open issues that we systematically catalog and relate to existing solutions and gaps. We conclude by outlining intriguing avenues for future research, such as multimodal integration, joint diarization and transcription, parameter-efficient foundation model adaptation, and fairness-aware evaluation. According to our analysis, the field is at a turning point where it can perform almost as well as humans in controlled environments but remains limited to acoustic complexity, linguistic diversity, and deployment realities.
