The growth of the fish is influenced by a variety of scientific factors. So, profit can be easily achieved by using some clever techniques, for example, maintaining the correct pH level along with the dissolved oxygen (DO) level and temperature, as well as turbidity for good growth of fish. Fully grown fish are generally sold at a good price because price of fish in the market is governed by weight as well as size of nurtured fish. Artificial intelligence (AI)-based systems may be created to regulate key water quality factors including salinity, dissolved oxygen, pH, and temperature. The software programme operates on an application server and is connected to multiparameter water quality meters in this system. This study examines smart fish farming methods that show how complicated science and technology may be simplified for use in seafood production. This research focuses on the use of artificial intelligence in fish culture in this setting. The technical specifics of DL approaches used in smart fish farming which includes data and algorithms as well as performance was also examined. In a nutshell, our goal is to provide academics and practitioners with a better understanding of the current state of the art in DL implementation in aquaculture, which will help them deploy smart fish farming applications as well their benefits.

The increase in number of people using the Internet leads to increased cyberattack opportunities. Advanced Persistent Threats, or APTs, are among the most dangerous targeted cyberattacks. APT attacks utilize various advanced tools and techniques for attacking targets with specific goals. Even countries with advanced technologies, like the US, Russia, the UK, and India, are susceptible to this targeted attack. APT is a sophisticated attack that involves multiple stages and specific strategies. Besides, TTP (Tools, Techniques, and Procedures) involved in the APT attack are commonly new and developed by an attacker to evade the security system. However, APTs are generally implemented in multiple stages. If one of the stages is detected, we may apply a defense mechanism for subsequent stages, leading to the entire APT attack failure. The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges. This survey paper will provide knowledge about APT attacks and their essential steps. This follows the case study of known APT attacks, which will give clear information about the APT attack process—in later sections, highlighting the various detection methods defined by different researchers along with the limitations of the work. Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.

Deep learning has enhanced the analysis of medical images but privacy issues and institutional variations restrict their large scale application in clinics. FedVLM, a federated vision language model tailored to privacy-preserving multimodal medical image analysis, is one of the solutions to these problems. Contrary to the conventional federated design, which can only process single modal image data, FedVLM takes paired radiological images and clinical reports jointly, which demonstrates high zero-shot and few-shot diagnostic performance. The design consists of secure aggregation, differential privacy and proximal optimization that ensure protection of patient data and minimize variability across sites. Large scale experiments on the NIH ChestX-ray14, MIMIC-CXR, and BraTS datasets indicate that FedVLM is an accurate and interpretable model that achieves near-centralized performance on vision language models without violating privacy. Building on previous works such as FACMIC, BioViL, and FAA-CLIP, FedVLM introduces new methods, including privacy-aware optimization, proximal regularization for varied data, and multimodal contrastive alignment, creating a unified federated framework for clear and secure medical image analysis. Although FedVLM shows promising performance, this work is currently at a research stage and is not yet ready for clinical use. We need validation through future multi-institutional clinical studies.

The explosive expansion of the Internet of Things (IoT) systems has increased the imperative to have strong and robust solutions to cyber Security, especially to curtail Distributed Denial of Service (DDoS) attacks, which can cripple critical infrastructure. The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabled mitigation to make intelligent, decentralized, and real-time DDoS countermeasures in an IoT network. The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms, which makes them more accurate in detection than statistical features alone, based on the Convolutional Neural Network (CNN) architecture, which can extract deep features. A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience. When tested on two test sets, BoT-IoT and IoT-23, the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate, which compares favorably to other solutions regarding effectiveness and the amount of time required to respond. Our findings support the feasibility of our method as an extensible and secure paradigm of next-generation IoT security, which has constrictive utility in mission-critical or resource-constrained settings. The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.