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
The term "cyber security" refers to the process of protecting computer networks from malicious online attacks or unauthorized access. Cyber security solutions are essential for organisations, enterprises, and governments due to the pervasive danger posed by cyber criminals. AI has immense potential as a viable solution for addressing this issue. By using the capabilities of artificial intelligence, security specialists can enhance their ability to protect susceptible networks and data from cyber assailants. This article provides an overview of the use of AI in the field of cyber security. AIdriven cyber security utilises AI and machine learning technology to enhance the safeguarding of computer systems and networks against cyber threats, including hacking, malware, phishing, and other types of assaults. AI-driven security solutions are specifically developed to automate the identification, examination, and handling of security breaches in real-time, thereby enhancing the efficiency and efficacy of cyber security. These systems provide the capability to process vast quantities of data, detect patterns and irregularities, and make prompt and precise choices, beyond the abilities of people alone. This enables organisations to proactively address emerging cyber risks.
The poultry industry plays a vital role in ensuring the global food supply, and efficient egg incubation is crucial for successful poultry farming. Traditional egg incubation methods have relied on manual monitoring and control, resulting in suboptimal hatch rates and increased labor requirements. However, the emergence of smart technologies has revolutionized the field of egg incubation, leading to the development of the Smart Egg Incubation System. The Smart Egg Incubation System integrates advanced technology, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), to optimize the incubation process and enhance hatch rates. The system employs a network of sensors to monitor and collect real-time data on temperature, humidity, egg turning, and ventilation within the incubation environment. This paper also proposes a commercial incubators website that will help to market incubators available for companies and hold their company profiles, where customers will tend to explore what they need pertaining to incubators and egg incubator accessories, helping to meet the needs of local farmers. This will help small enterprises to grow and get affordable incubators and accessories to eliminate the distance in business affairs for both local farms and big farms. An AI chatbot will also be available specifically designed to assist farmers in monitoring and managing chicken incubators effectively. The chatbot aims to provide real-time information and support to farmers, addressing their needs related to purchasing incubators, understanding incubator capacities, hatching periods, and addressing miscellaneous questions. The AI chatbot acts as a reliable virtual assistant, offering farmers guidance on where to purchase suitable incubators tailored to their specific requirements. By analyzing a comprehensive database of reputable suppliers and their offerings, the chatbot offers recommendations based on factors such as incubator size, cost, features, and customer reviews.
Plant diseases pose a major threat to agricultural productivity and economies dependent on it. Monitoring plant growth and phenotypes is vital for early disease detection. In Indian agriculture, black-gram (Vigna mungo) is an important pulse crop afflicted by viral infections like Urdbean Leaf Crinkle Virus (ULCV), causing stunted growth and crinkled leaves. Such viral epidemics lead to massive crop losses and financial distress for farmers. According to the FAO, plant diseases cost countries $220 billion annually. Hence, there is a need for quick and accurate diagnosis of crop diseases like ULCV. Recent advances in computer vision and image processing provide promising techniques for automated non-invasive disease detection using leaf images. The key steps involve image pre-processing, segmentation, informative feature extraction, and training machine learning models for reliable classification. In this work, an automated ULCV detection system is developed using black gram leaf images. The Grey Level Co-occurrence Matrix (GLCM) technique extracts discriminative features from leaves. Subsequently, a deep convolutional neural network called YOLO (You Only Look Once) is leveraged to accurately diagnose ULCV based on the extracted features. Extensive experiments demonstrate the effectiveness of the GLCM-YOLO pipeline in identifying ULCV-infected leaves with high precision. Such automated diagnosis can aid farmers by providing early disease alerts, thereby reducing crop losses due to viral epidemics.
In the realm of data retrieval, sparse vectors serve as a pivotal representation for both documents and queries, where each element in the vector denotes a word or phrase from a predefined lexicon. In this study, multiple scoring mechanisms are introduced aimed at discerning the significance of specific terms within the context of a document extracted from an extensive textual dataset. Among these techniques, the widely employed method revolves around inverse document frequency (IDF) or Term Frequency-Inverse Document Frequency (TF-IDF), which emphasizes terms unique to a given context. Additionally, the integration of BM25 complements TF-IDF, sustaining its prevalent usage. However, a notable limitation of these approaches lies in their reliance on near-perfect matches for document retrieval. To address this issue, researchers have devised latent semantic analysis (LSA), wherein documents are densely represented as low-dimensional vectors. Through rigorous testing within a simulated environment, findings indicate a superior level of accuracy compared to preceding methodologies.
Artificial intelligence has a variety of applications in the healthcare sector due to its capacity to assist in not only treatment and especially operations, but also diagnostics and prevention. There is hardly any industry that does not benefit from an increase in IT resources and self-learning computer networks. This paper presents smart healthcare data which can assess the situation and determine whether or not a person requires immediate medical assistance, as well as whether or not their symptoms will likely improve on their own over time. In addition to its contributions to treatment, operations, diagnostics, and prevention, Artificial Intelligence (AI) plays a pivotal role in optimizing healthcare processes and resource utilization. The integration of AI technologies in the healthcare sector has facilitated enhanced data analysis, personalized treatment plans, and more efficient allocation of medical resources. The intelligent processing of vast amounts of patient data enables timely identification of emerging health trends and the development of targeted interventions. Moreover, AI-powered healthcare data systems have the potential to revolutionize patient care by providing real-time assessments of individual health statuses. These systems can not only discern the need for immediate medical assistance but also predict the likelihood of symptom improvement over time, aiding in the formulation of proactive and preventive healthcare strategies. The ongoing advancements in AI continue to refine these capabilities, fostering a future where technology and healthcare seamlessly converge for the betterment of patient outcomes and overall public health.
