International Journal of Information Technology最新文献

The paper presents new design and implementation for Web messages static clustering based on TF-IDF and Minkowski Distance metric. The target of the proposed Minkowski clustering is to empower Web messages aggregators in order to reduce network traffic by aggregating highly similar messages. Web services (W.S.) technology offers an extensive platform for representing, discovering, and calling services in many environments, including Service Oriented Architectures (SOA). The basis of W.S. technology is built upon several XML-based protocols, such as the Simple Object Access Protocol (SOAP), which effectively guarantees W.S. flexibility, transparency, and harmony. There is an increasing demand to enhance the efficiency of online services. It is mainly limited by the over utilization of XML. SOAP communications lead to high network congestion. Furthermore, they cause higher latency and processing delays when compared to alternative technologies. Previous studies have proposed XML clustering techniques to support compression- aggregation models. Technically, aggregation can decrease the overall size the SOAP messages, hence decreasing the needed bandwidth across clients and servers.

We aim to effectively solve and improvise the Meta Meme Challenge for the binary classification of hateful memes detection on a multimodal dataset launched by Meta. This problem has its challenges in terms of individual modality processing and its impact on the final classification of hateful memes. We focus on feature-level fusion methodologies in proposing the solutions for hateful memes detection in comparison with the decision-level fusion as feature-level fusion generates richer features’ representation for further processing. Appropriate model selection in multimodal data processing plays an important role in the downstream tasks. Moreover, inherent negativity associated with the visual modality may not be detected completely through the visual processing models, necessitating the differently processed visual data through some other techniques. Specifically, we propose two feature-level fusion-based methodologies for the aforesaid classification problem, employing VisualBERT for the effective representation of textual and visual modality. Additionally, we employ image captioning generating the textual captions from the visual modality of the multimodal input which is further fused with the actual text associated with the input through the Tensor Fusion Networks. Our proposed model considerably outperforms the state of the arts on accuracy and AuROC performance metrics.

Counterfeit merchandise poses significant challenges for both consumers and retailers. When counterfeit goods infiltrate the market, they damage the trustworthiness and reputation of legitimate companies, leading to negative publicity. Furthermore, these imitations can be harmful, especially in critical sectors like food and pharmaceuticals. To address this issue, it is essential to identify and prevent counterfeit products from reaching consumers. Our proposed solution leverages blockchain technology to authenticate products. Blockchain’s decentralized database securely stores all transaction data, ensuring transparency and traceability. Additionally, we introduce a tool that records ownership and product details. By utilizing a Quick Response (QR) code, consumers can easily verify the authenticity of a product, thus accessing its manufacturing and ownership information. This approach not only safeguards consumer safety but also protects the reputation and financial performance of legitimate business.

This manuscript tackles brain carcinoma diagnosis through a multi-attribute decision-making lens. Using complex q-rung orthopair trapezoidal fuzzy sets, we develop tailored aggregation operators and explore their significance. We delve into idempotency theory, highlighting instances where monotonicity and boundedness fail. Building on these operators, we propose a novel methodology for fuzzy environment decision-making. Applied to medical diagnosis, we identify the most dangerous brain carcinoma type, showcasing practical utility. Comparative analyses confirm the superiority of our technique, promising advancements in diagnosis methodologies.

The exploration of clinical notes has garnered attention, primarily owing to the wealth of unstructured information they encompass. Although substantial research has been carried out, notable gaps persist. One such gap pertains to the absence of work on real-time Indian data. The work commenced by initially using Medical Information Mart for Intensive Care (MIMIC III) dataset, concentrating on diseases such as Asthma, Myocardial Infarction (MI), and Chronic Kidney Diseases (CKD), for training the model. A novel model, transformer-based, was built which incorporated knowledge of abbreviations, symptoms, and domain knowledge of the diseases, named as SM-DBERT + + . Subsequently, the model was applied to an Indian dataset using transfer learning, where domain knowledge extracted from Indian sources was utilized to adapt to domain differences. Further, an ensemble of pre-trained models was built, applying transfer learning principles. Through this comprehensive methodology, we aimed to bridge the gap pertaining to the application of deep learning techniques to Indian healthcare datasets. The results obtained were better than fine-tuned Bidirectional Encoder Representations from Transformers (BERT), Distilled BERT (DISTILBERT) and A Lite BERT (ALBERT) models and also other specialized models like Scientific BERT (SCI-BERT), Clinical Biomedical BERT (Clinical Bio-BERT), and Biomedical BERT (BIOBERT) with an accuracy of 0.93 when full notes were used and an accuracy of 0.89 when extracted sections were used. It has demonstrated that model trained on one dataset yields good results on another similar dataset as this model incorporates domain knowledge which could be modified during transfer learning to adapt to the new domain.

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models.

The research paper introduces an algorithm called SAFARM which performs association rule mining with the help of simulated annealing. It’s a multi-objective problem with vast search space. The suggested approach is independent of the database as it does not require minimum support or minimum confidence specification. In the algorithm, a fitness function is designed to fulfill the required objective and the presentation of rules is proposed with a compact structure. The correctness and efficiency of the algorithm is verified by testing it on synthetic and real databases.

The rapid evolution of network traffic created various problems in detecting Distributed Denial of Service (DDOS) attacks. The manifestation of Software Defined Networking (SDN) provides some individuality in that the SDN Controller uses a technique to examine the acquired data from the Flow Table (FT). As traffic increases, the controller's processing capability decreases, resulting in insufficient space availability for the FT and flow buffer. Understanding the struggles that exist in the controller and FT, this study provided a distinctive procedure that will increase performance, reduce controller load, manage FT space, and flow buffers that are activated by the Virtual Controller (VC). It dynamically completes the bundle of packets received at the router/switch, analyze the FT using the Attack Level Detection (ALD) method, assesses the bandwidth utilization of a particular user, and maps to the ingress port. The ALD algorithm detects mismatched packets and congested packets originating from faked IP and network addresses. This effort is related with the regular scenario and the attack level scenario, which use a mininet simulator with two controllers, the POX controller and the Open Daylight controller, to simulate major performance variations. This study efficiently lowers the overload of the VC and FT, hence preventing the DDoS assault employing VC.