Heterogeneous database integration is the study of integrating data from multiple databases. Integrating the heterogeneous database of the same domain has three main challenges that make the heterogeneity problem difficult to solve. The three problems are Semantic, Syntactic and Structural Heterogeneity. Conventional heterogeneous database integration schemes, like De-duplication Techniques, Data Warehouse, and Information Retrieval (IR) Search technique lack the capability to solve the integration of databases completely. The only reason is they cannot deal with Semantic heterogeneity problems efficiently. The semantic Web ontology model is experimented and discussed in the article, which is based on the query execution model. The ontology modeling is divided into two phases, initially to translate the database rules according to ontology rules to find an abstract ontology model. Secondly, to extend the abstract ontology model according to the databases. The method facilitates to apply similarly SPQRAL queries to search the data in the databases. Therefore, the Jena API is used to retrieve semantically similar records. The experiment is based on the two heterogeneous Universities Library Databases. The results show the effectiveness and scalability of the methodology.

The growth of the internet and technology has had a significant effect on social interactions. False information has become an important research topic due to the massive amount of misinformed content on social networks. It is very easy for any user to spread misinformation through the media. Therefore, misinformation is a problem for professionals, organizers, and societies. Hence, it is essential to observe the credibility and validity of the News articles being shared on social media. The core challenge is to distinguish the difference between accurate and false information. Recent studies focus on News article content, such as News titles and descriptions, which has limited their achievements. However, there are two ordinarily agreed-upon features of misinformation: first, the title and text of an article, and second, the user engagement. In the case of the News context, we extracted different user engagements with articles, for example, tweets, i.e., read-only, user retweets, likes, and shares. We calculate user credibility and combine it with article content with the user’s context. After combining both features, we used three Natural language processing (NLP) feature extraction techniques, i.e., Term Frequency-Inverse Document Frequency (TF-IDF), Count-Vectorizer (CV), and Hashing-Vectorizer (HV). Then, we applied different machine learning classifiers to classify misinformation as real or fake. Therefore, we used a Support Vector Machine (SVM), Naive Byes (NB), Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and K-Nearest Neighbors (KNN). The proposed method has been tested on a real-world dataset, i.e., “fakenewsnet”. We refine the fakenewsnet dataset repository according to our required features. The dataset contains 23000+ articles with millions of user engagements. The highest accuracy score is 93.4%. The proposed model achieves its highest accuracy using count vector features and a random forest classifier. Our discoveries confirmed that the proposed classifier would effectively classify misinformation in social networks.

Introduction: Drug-drug interactions (DDIs) are a major cause of preventable harm in polypharmacy and remain difficult to anticipate as formularies, indication profiles, and interaction labels evolve. Over the last few years, the DDI modeling landscape has shifted rapidly toward graph-native, multimodal, and contrastive or self-supervised learning, alongside renewed interest in extraction, decision support, and pharmacovigilance pipelines. Objective: This systematic literature review (SLR) synthesizes computational work on DDI prediction, event-type classification, text extraction, and safety signal detection published between 2022 and 2025. We aim to (i) organize recent methods into a feature–method taxonomy, (ii) compare their evaluation setups and reported performance, and (iii) assess progress on generalization, explainability, and clinical translation. Methods: Using a prespecified review protocol and PRISMA 2020 reporting guidance, we searched major bibliographic databases and screened peer-reviewed studies that proposed or evaluated computational methods for DDIs or closely related interaction tasks. Eligible work spans molecular graph and descriptor models, multimodal pharmacological representations, heterogeneous and knowledge graphs, text-based extraction and retrieval, and real-world evidence from EHRs, FAERS, and similar sources. We grouped methods into similarity and matrix-factorization baselines, conventional machine learning, deep neural architectures (CNNs, RNNs, and Transformers), graph neural networks and knowledge-graph representation learning, multimodal fusion, contrastive/self-supervised objectives, and emerging LLM-based frameworks. For each study, we extracted feature modalities, tasks, datasets and splits, metrics, explainability tools, and any form of clinical or user-centred evaluation. Results: Recent work consistently reports improved AUROC/AUPR on DrugBank-derived, TWOSIDES-like, and DDIExtraction benchmarks, driven by substructure-aware GNNs, KG-augmented architectures, multimodal fusion, and inductive or out-of-distribution training regimes. However, most models still rely on a small set of public datasets, heterogeneous and sometimes optimistic split protocols, and limited external or prospective validation. Event-level and long-tailed risk modeling, prompt- or prototype-based learning, and LLM-assisted extraction strengthen coverage of rare but clinically important interaction types, yet uncertainty quantification, label quality assessment, and end-to-end integration into prescribing workflows remain underexplored. Discussion: Between 2022 and 2025, DDI modeling has moved decisively toward graph-centric, multimodal, and contrastive/self-supervised paradigms that clearly advance benchmark performance but only partially close the gap to reliable, mechanism-aware clinical decision support. We distill design guidelines and a research agenda around transparent dataset construction, realistic and standardized evaluation protocols, mechanism- and direction-aware modeling, robustness to novel drugs and regimens, and prospective, clinician-in-the-loop validation.

Specifically, in the past few years, internet traffic has grown rapidly, evolving modern network technologies with hybrid telecommunication systems and conventional computer networks. Unfortunately, the wireless nature of these technologies provides easy access to the network, resulting in an increased risk of network intrusion, ultimately creating a demand to develop an intrusion detection system (IDS). In this paper, an IDS is proposed to detect the malicious flows in the Software-Defined Network (SDN). The core concept behind this idea is implementing a robust statistical analysis-based intrusion detection system (SF-ABIDS) inside the RYU controller that takes statistics of network traffic from the southbound interface after a specific time interval (without changing the standard architecture of SDN). It is evident that due to the centralized nature of SDNs, the SDN controller that sits on top will face lightning-speed incoming network traffic flows. Our IDS will live in SDN controller as an application, and it will perform systemized analysis on incoming network traffic flows. After research, the IDS will have the results, and it will completely block the generator of IP that is classified as malicious by our IDS. This will be a generalized workflow of our IDS in an SDN controller monitoring the incoming traffic. This workflow will allow our IDS to perform accurately and achieve outstanding results by classifying malicious packets and placing them to where they belong i.e., The Blacklist. The SF-ABIDS is inspired by a meta-classification (an ensemble classification) technique that consists of four modules. We use a standard ISCX-UNB dataset to gauge the overall performance of our proposed Intrusion Detection System. Flow match statics features are extracted using the open flow (OF) protocol, which enables the new scheme to detect malicious flows in less time with higher accuracy. These features are then exploited using various ML (Machine Learning) based classifiers, including Decision Table, JRip, J48, PART, Random Forest, RepTree, LMT. The performance of these classifiers is tested by using evaluation parameters like accuracy in terms of true positive (TP), false positive (FP), AUROC, and the harmonic mean of Precision and recall at 0.95. Supervised classifiers with more than 99% AUROC, harmonic value, accuracy, and detecting the flow class in the least time (up to precision level 3) is considered ideal for the new system. As we mentioned above, the architecture of Software defines network enforces that it should be dependable to manage the network traffic, so our proposed Intrusion Detection System will introduce dependability as security in SDN by actively monitoring incoming traffic. So, intruders cannot exploit the centralized nature of Software Defined Networks. The core idea of the transition from conventional networks to SDNs is to introduce simplicity so the network can be easily scalable to support the need of today due to IoT (Internet of things) revolution. Our IDS supports the manifesto and introduces simplicity and security in the network without any additional pre-processing overhead.