Empowering Cybersecurity: A Deep Dive into AI-Driven Security Intelligence Modelling
Smart Incubator for Empowering Poultry Farmers by Maximizing Egg Hatching Success
Early Disease Detection of Black Gram Plant Leaf using Cloud Computing Based YOLO V8 Model
Data Retrieval in Cancer Documents using Various Weighting Schemes
Securing Healthcare Data in Smart Cities: A Machine Learning Approach
Emotions are states of readiness in the mind that result from evaluations of one's own thinking or events. Although almost all of the important events in our lives are marked by emotions, the nature, causes, and effects of emotions are some of the least understood parts of the human experience. Emotion recognition is playing a promising role in the domains of human-computer interaction and artificial intelligence. A human's emotions can be detected using a variety of methods, including facial gestures, blood pressure, body movements, heart rate, and textual data. From an application standpoint, the ability to identify human emotions in text is becoming more and more crucial in computational linguistics. In this work, we present a classification methodology based on deep neural networks. The Bi-directional Gated Recurrent Unit (Bi-GRU) employed here demonstrates its effectiveness on the Multimodal Emotion Lines Dataset (MELD) when compared to Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). For word encoding, a comparison of three pre-trained word embeddings namely Glove, Word2Vec, and fastText is made. The findings from the MELD corpus support the conclusion that fastText is the best word embedding for the proposed Bi-GRU model. The experiment utilized the "glove.6B.300d" vector space. It consists of two million word representations in 300 dimensions trained on Common Crawl with sub-word information (600 billion tokens). The accuracy scores of GloVe, Word2Vec, and fastText (300 dimensions each) are tabulated and studied in order to highlight the improved results with fastText on the MELD dataset tested. It is observed that the Bidirectional Gated Recurrent Unit (Bi-GRU) with fastText word embedding outperforms GloVe and Word2Vec with an accuracy of 79.7%.
Electronic resource information licensing challenges libraries as they acquire new resources in academic institutions. There are some crucial key components of a licensing agreement, such as access concerns, use of an electronic information resource, vendor support and technical considerations, cancellation and termination, legal issues, and the review and renewal process. Developing licensing guidelines makes licensing work more accessible by providing more consistency in the approach to license review and by making the practice and policy of the institution on the basis of the licensing review.
In today's fast-paced business environment, efficient Information Technology (IT) service management is crucial for maintaining a competitive edge. The latest Information Technology Service Management (ITSM) solutions offer a wide range of features and capabilities, including automation, self-service portals, and analytics, to help organizations streamline their IT operations and improve their overall performance. This review will take a closer look at some of the latest IT Service Management (ITSM) solutions on the market and explore their key features and benefits. It will also discuss how these solutions can help organizations improve their IT service delivery and support their digital transformation efforts.
Thermography, also referred to as thermal imaging, is a technique for identifying breast irregularities through the capturing of temperature patterns using an infrared camera. It is being explored as a potential tool for diagnosing breast cancer, particularly in young women with thick breasts, due to the limitations of mammography. However, it is important to note that thermal imaging is not currently considered a replacement for mammography and should be used in conjunction with other diagnostic tools such as mammography and ultrasound. Further research is necessary to determine the effectiveness of thermal imaging in diagnosing breast cancer.
The agriculture sector needs precise data and advanced technology in a digitally informed scenario. The agriculture industry is being advanced with the support of information and communication technologies, such as the internet of things. Advanced sensors can be applied for specific agricultural management practices like insect management, disease management, land preparation, irrigation systems, and the control of smart farms and smart greenhouses. The aim is to increase yield, optimize farm inputs, provide market and government policy related information, and enable digital literacy among farmers, which would be helpful in enhancing rural income and sustainable agriculture. It is a need to promote digital interactive information systems in real time, like text messages, mobile apps, or interactive audio-video modes, to provide agricultural information to extension workers, farmers, academicians, and researchers through different means. The use of information and communications technologies, such as blockchain technologies, data mining, and wireless sensors, will definitely help in achieving different goals in sustainable agriculture. This article will provide summarized information related to the benefits and usage of technology in different agriculture sectors.
