The adoption of blockchain technology provides significant disruptive benefits to internet-of-things (IoT) applications in healthcare in vital aspects like security, integrity, transparency, and efficiency. Nevertheless, in order to fully realize the potential of blockchain-driven solutions, healthcare organizations have to address intricate compromises between essential factors including scalability, privacy and resource utilization considering that the data sensitivity alongside strict regulatory compliance requirements characterize this sector. This research discusses the fundamental aspects of these trade-offs, including the range of consensus protocols (e.g. proof-of-work, proof-of-stake) and cryptographic techniques (e.g. zero-knowledge proofs, homomorphic encryption). A systematic choice matrix is created, which relates specific use cases of the healthcare IoT to the optimal tailored blockchain structures on such critical metrics as transaction volume, frequency, privacy level and resource restrictions. The suggested framework provides solid, actionable recommendations to healthcare organizations in order to help them benefit from the enormous promise of the blockchain for connected IoT healthcare by finding a balance between decentralization advantages and performance, security and compliance requirements.

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).

Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics.

Cyberbullying has escalated due to social media's rapid growth, endangering internet security. Correct these harmful habits. ML is used to research cyberbullying on Twitter. This model is enhanced with adaptive external dictionary (AED). Terms that are negative and positive are produced manually. The dynamic lists of positive and negative words produced by AED sentiment analysis. The dataset has positive and negative tweet columns. Social media's fast expansion has increased cyberbullying, threatening online safety. Recognizing and addressing these risky activities quickly requires a comprehensive system. Uses ML to detect Twitter cyberbullying (ML). This model detects better using Adaptive External Dictionary.47K Kaggle tweets made the AED. Manual refinement only produces negative and positive phrases in the first portion, relevant to our topic. AED sentiment analysis creates dynamic lists of Positive Words (PW) and Negative Words (NW) in this study. Tweets are columns. Combining internet data with positive and negative word counts identifies cyberbullying.

The super-resolution (SR) of Magnetic Resonance Imaging (MRI) is essential for utilizing clinical scans with limited resolution, noise, and anisotropic sampling, such as multi-modal brain tumor imaging. In this work, we propose a Tri-Representation hybrid framework for MRI SR, TRT-GLA, that redefines the MRI SR task as a joint spatial–spectral–structural high-resolution image generation problem. TRT-GLA utilizes (i) spatial global–local attentions for modeling the spatial anatomy, (ii) a Fourier spectral transfer mechanism for upholding spectral consistency, and (iii) multi-scale hierarchical spectral decomposition for improved edge details. To adapt the learning framework to medical imaging characteristics, we introduce a tri-representation consistent loss function that explicitly combines pixel-wise, spectral, and edge structure priors from the high-resolution ground-truth, as well as a progressive resolution learning strategy. Our large-scale brain tumor experiments, on the IXI, BraTS 2019, 2020, and 2023 datasets, show that TRT-GLA achieves state-of-the-art results at upsampling factors of ×2, ×4, and ×8, respectively, achieving substantial improvements across CNN, GAN, and transformer-based methods in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Multi-scale Structural Similarity Index (MS-SSIM). We further demonstrate how SR benefits brain tumor segmentation through the downstream task evaluation of a dual-branch segmentation framework. TRT-GLA produces highly accurate tumor segmentation results from low-resolution inputs, improving over native high-resolution inputs at ×8 in critical tumor boundary regions and in small tumor regions. There remains a small gap between native, high-resolution imaging and SR-enhanced performance, which TRT-GLA nearly closes under realistic scenarios. Our results highlight the importance of synthesizing unified priors over spatial, spectral, and structural domains within a transformer for anatomically faithful reconstructions. Importantly, we also establish the utility of TRT-GLA in supporting quantitative analysis through a downstream tumor segmentation experiment that is clinically relevant.