The rapid growth of digital communications and extensive data exchange have made computer networks integral to organizational operations. However, this increased connectivity has also expanded the attack surface, introducing significant security risks. This paper provides a comprehensive review of Intrusion Detection System (IDS) technologies for network security, examining both traditional methods and recent advancements. The review covers IDS architectures and types, key detection techniques, datasets and test environments, and implementations in modern network environments such as cloud computing, virtualized networks, Internet of Things (IoT), and industrial control systems. It also addresses current challenges, including scalability, performance, and the reduction of false positives and negatives. Special attention is given to the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), and the potential of distributed technologies such as blockchain. By maintaining a broad-spectrum analysis, this review aims to offer a holistic view of the state-of-the-art in IDSs, support a diverse audience, and identify future research and development directions in this critical area of cybersecurity.

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to ensure the suitability of the model construction for IDS usage. The evaluation was conducted for both binary and multi-class classification to ensure the consistency of the selected ML algorithms for the given dataset. The experimental results demonstrated accuracy rates of 100% for binary classification and 99.4In conclusion, it can be stated that supervised machine learning algorithms exhibit high and promising classification performance based on the study of three popular datasets.

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks.

The early detection of fire and smoke is essential for mitigating human casualties, property damage, and environmental impact. Traditional sensor-based and vision-based detection systems frequently exhibit high false alarm rates, delayed response times, and limited adaptability in complex or dynamic environments. Recent advances in deep learning and computer vision have enabled more accurate, real-time detection through the automated analysis of flame and smoke patterns. This paper presents a comprehensive review of deep learning techniques for fire and smoke detection, with a particular focus on convolutional neural networks (CNNs), object detection frameworks such as YOLO and Faster R-CNN, and spatiotemporal models for video-based analysis. We examine the benefits of these approaches in terms of improved accuracy, robustness, and deployment feasibility on resource-constrained platforms. Furthermore, we discuss current limitations, including the scarcity and diversity of annotated datasets, susceptibility to false alarms, and challenges in generalization across varying scenarios. Finally, we outline promising research directions, including multimodal sensor fusion, lightweight edge AI implementations, and the development of explainable deep learning models. By synthesizing recent advancements and identifying persistent challenges, this review provides a structured foundation for the design of next-generation intelligent fire detection systems.