Maharaja Surajmal Institute of Technology

The benefits of the Internet of Medical Things (IoMT) in providing seamless healthcare to the world are at the forefront of technological advancement. However, security concerns of any IoMT systems are high since they threaten to compromise personal information of patients and can even cause health hazards. Researchers are exploring the use of various techniques to ensure a high level of security of IoMT systems. One key concern is that the computing power of any Internet of Things (IoT) device is relatively low, hence mechanisms that require low computational power are appropriate for designing Intrusion Detection Systems (IDS). In this research work, a blockchain IDS coalition is proposed for securing IoMT networks and devices. The blockchain ledger is compact and uses less processing resources. Additionally, the ledger requires less communication overhead. The cryptographic hashes in the suggested architecture ensure complete data secrecy and integrity between parties who are trusted and those who are untrustworthy. Peer-to-peer networks in both central and cluster networks are also included in this work for complete decentralization. The proposed model can counter various attacks, including Denial of Service (DoS), anonymity attacks, impersonation attacks, Man-In-The-Middle (MITM), and Cross-Site Scripting (XSS). The proposed method achieved an F1- score as high as 100% and reported an AUC value of over 99%.

Intrusion detection has become indispensable for modern cybersecurity as evolving cyberattacks continue to threaten networked environments. The rapid escalation of sophisticated cyberattacks has created an urgent need for intelligent intrusion detection approaches capable of identifying threats with high accuracy and low latency. In this paper, we propose a new method to detect the cyberattacks in network traffic by utilizing a three-tier Deep Learning (DL) approach and hybrid optimization algorithm. The proposed three-level DL framework consists of Convolutional Neural Networks (CNNs) for spatial feature extraction, Support Vector Machines (SVMs) for the reliable classification, and Siamese Recurrent Neural Networks (SRNNs) for pairwise similarity learning to perform detection of known and zero-day attacks. Using GPSA (Golden Pelican Search Algorithm) as a hybrid optimization model to select features and tune hyperparameters further improves performance. It combines the exploratory characteristics of the Golden Jackal Optimization (GJO) with the exploitation abilities of the Pelican Optimization Algorithm (POA). Experimental results on the standard cybersecurity datasets show that our model achieves better accuracy, precision, sensitivity and specificity in comparison to existing algorithms. This paper has presented the potential of combining multi-tier DL with bio-inspired hybrid optimization for next-generation network systems.

This paper dives into how nanotechnology is chang-ing the game in medicine and healthcare. By working with materials at the nanoscale – that’s one-billionth of a meter – scientists can tweak physical, chemical, and biological properties in ways that are really different from larger materials. The study takes an interdisciplinary approach, pulling together data from research articles, clinical trials, and case studies since 2010 to showcase the latest in nanomedicine, biotechnology, and genetic engineering. Key findings show that nanotech is fantastic for improving diagnostic accuracy, allowing for early detection of diseases at the cellular and molecular level. When it comes to treatment, the paper talks about how engineered nanoparticles can really enhance drug delivery systems, making medications more soluble, stable, and effective while reducing side effects through targeted delivery. For example, using magnetic drug delivery and specialized nanoparticles in cancer treatment can directly target tumors and help with neurological issues, but there are still some challenges with non-specific accumulation and potential toxicity. The paper also looks into exciting areas like regenerative medicine, monitoring health in real-time with nanosensors, and the possible creation of nanorobots for targeted cell destruction. Even though there’s a lot of promise, the study points out some major roadblocks for getting these technologies into clinical use, like concerns over biocompatibility, the ability to scale up manufacturing, and the strict regulatory hoops they need to jump through. In short, while nanotechnology has incredible potential to change personalized medicine and disease prevention, ongoing research across different fields is crucial to tackle the current technical and safety hurdles. Index Terms—nanotechnology, nanomedicine, drug-delivery, diagnostics, healthcare, regenerative medicine

The stability of power grid systems can be significantly affected by the unpredictability and volatility of power generation; however, accurate forecasting of solar energy power can help reduce this impact. This benefits the system through lower operating costs, balanced operation, and optimal dispatch. Over the past decade, extensive research has been published on this topic, exploring physical models, artificial intelligence (AI) techniques, and numerical and probabilistic approaches. Additionally, previous review studies centred their review discussions on a specific event horizon, others focused exclusively on the geographical horizon, and assessed only particular classes of photovoltaic (PV) output power forecasts. They paid little or no attention to other classes. Therefore, a thorough analysis of solar PV output power forecasting methods is required. In this paper, special focus is given to deep learning (DL), machine learning (ML), and hybrid methods, as these AI areas are gaining popularity. This study aims to provide a comprehensive and critical review of the latest AI applications. It also features a statistical analysis of forecasting errors based on over a hundred solar generation forecast studies. Additionally, the paper offers a brief introduction to the metrics used in ML, DL, and hybrid methods and their interpretation. A discussion of factors influencing forecasting errors is included. Future models will be more accurate because of the clarification that has been provided.

Abstract Crystal Violet is a toxic dye commonly found in wastewater from textile and printing industries. It is harmful to both the environment and human health due to its non-biodegradable and carcinogenic nature. Removing such dyes from water is a major challenge. In this study, we used a process called photocatalysis, where light energy helps to break down harmful chemicals, to degrade Crystal Violet dye. Titanium dioxide (TiO₂), a well-known photocatalyst, was used as the base material. To improve its efficiency, we added small amounts of two rare earth elements Cerium (Ce) and Samarium (Sm) to the TiO₂ structure. The doped TiO₂ photocatalysts were prepared using the co-precipitation method, which allows for even distribution of the dopant metals in the material. After synthesis, the materials were analyzed using different techniques to understand their crystal structure, surface properties, light absorption, and electron movement during the reaction. Photocatalytic activity was tested under UV light to evaluate how effectively the materials could degrade Crystal Violet. We studied the effects of various factors such as catalyst amount, dye concentration, solution pH, and light exposure time. The results showed that both Ce- and Sm-doped TiO₂ performed significantly better than undoped TiO₂. Over 90% of the dye was removed in a short time, with samarium-doped TiO₂ showing slightly better results than cerium-doped TiO₂ due to better electron trapping and redox properties. This study suggests that doping TiO₂ with rare earth metals is a promising and eco-friendly method for treating dye-contaminated wastewater.