Municipal solid waste (MSW) management is an essential element of present-day society. The proper storage and disposal of solid waste is critical to public health, safety, and environmental performance. The direct recovery of MSW into useful energy is a critical task. In addition, the demand for conventional power supplies is high. As a strategy to solve these two problems, the technology to directly convert municipal solid waste into conventional energy to replace fossil fuels has been obtained. The hydrothermal carbonization (HTC) process is a thermochemical conversion process that utilizes heat to convert wet biomass feedstocks into hydrochar. Hydrochar with premium gasoline properties is used for fuel combustion for strength. The properties of fuel hydrochar, including C char (carbon content), HHV (higher heating value), and yield, are mainly based on the properties of the MSW. This study aimed to predict the properties of fuel hydrochar using a machine learning (ML) model. We employed an ensemble support vector machine (E-SVM) as the classifier, which was combined with the slime mode algorithm (SMA) for optimization and developed based on 281 data points. The model was primarily trained and tested on a fusion of three datasets: sewage sludge, leftovers, and cow dung. The proposed ESVM_SMA model achieved an excellent overall performance with an average R² of 0.94 and RMSE of 2.62.

Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.

Wireless ad-hoc networks operate independently of existing infrastructure, using devices like access points to connect end-user computing devices. Current methods face issues such as low detection accuracy, structural deviation, and extended processing times. This paper proposes a cross-layer approach that leverages knowledge from the physical and Medium Access Control (MAC) layers, which is then shared with higher layers to effectively mitigate wormhole and blackhole attacks. A wormhole attack disrupts communication through tunneling, while a blackhole attack manipulates network traffic by impersonating the source. The proposed cross-layer framework integrates the network, MAC, and physical layers, and is independent of specific network protocols. The physical layer handles channel interference, the network layer manages process handling, and the MAC layer oversees bandwidth information and tracks failed transmissions. Performance metrics are measured in seconds. The Enhanced Support Vector Machine (E-SVM) algorithm, implemented using NS3 software, demonstrates superior performance compared to traditional SVM techniques across multiple metrics, including average energy consumption, average remaining energy, packets received, packet delivery ratio, delay, jitter, throughput, normalized overhead, dropping ratio, and goodput. Simulation results show that E-SVM achieves a 12.5% dropping ratio, 98.459% energy consumption, and an 89.2879% packet delivery ratio, outperforming existing SVM techniques across various network sizes.

With the use of Large Language Models (LLMs) becoming more significant in many applications, it has become a great need to ask the question how these LLMs can be made safe and robust, and hence make it behave in an ethical way. Among the most seen and evident vulnerabilities or loopholes in LLM are the attacks termed as prompts injections, which is a key vulnerability. Even though this issue is being extensively studied, most of the available and existing literature and defense systems are mostly focussed on English language prompts. As of now, there are no publicly available datasets or any known defense mechanisms that works with the aim of detecting and/or preventing the so-called prompt injection in Indian regional or code-mixed languages associated. The project aims to identify or propose a mechanism to find an answer on exactly how to overcome this significant research gap by assessing the reaction of various LLMs to the injection-style prompts typed in Indian languages particularly Hindi and its code-mixed version, Hinglish. A clean dataset of size 4000 prompts including safe and malicious prompts in Hindi and Hinglish (Hindi-2000 prompts and Hinglish-2000 prompts) is made and a hybrid classifier is proposed that finds to detect injection attempts using their language patterns and other intents or features. Various algorithms including Transformer, Distil Transformer, SVM, XGBoost, Random Forest and Logistic Regression are trained and tested. The integration of a rule-based layer and a transformer model was shown to perform better than all baselines, with 99.70% accuracy and high precision (99.65%), recall (99.75%), and F1-score (99.70%), which demonstrates that its detection is very reliable. The system provides a new direction in ensuring the security of LLM interfaces against prompt-based injection attacks, especially in a diverse and multilingual user population like that in India.