The tremendous growth of cyberspace has magnified the risk of advanced cyber-attacks, making the conventional security system deficient. An intrusion detec

The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.

It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

The authors wish to make the following corrections to their paper

Timely diagnosis and efficient treatment planning depend on the early and precise detection of bone cancers from X-ray imaging.Using the publicly accessible Bone Tumor X-ray Radiograph Dataset (BTXRD), this study assesses deep learning models for both binary (normal vs. tumor) and multiclass (normal, benign, malignant) classification: MobileNetV2, EfficientNetB0, and a hybrid ResNet50 + Transformer. All models reached stable convergence for binary classification, however the hybrid ResNet50+Transformer model outperformed the others (AUC = 0.80, accuracy = 71%). After targeted augmentation and class weighting, the same model demonstrated higher discrimination (macro-AUC = 0.91) in the multiclass test, especially enhancing memory for malignant cases. The results demonstrate that complex bone tumor patterns are better represented when convolutional feature extraction and transformer based attention are combined. This method shows how hybrid CNN–Transformer frameworks can be used for accurate and effective radiograph based bone tumor screening.