In big cloud data centers, the best physical machine (PM) is selected using the Virtual Machine Placement(VMP) process. To address this issue, a number of approaches have been proposed. Nevertheless, the existing solutionsonly take into account a small number of resource categories, which leads to an uneven load and ultimately, theactivation of superfluous physical computers within the data center. The aim of this research is to maximize resourceusage while lowering power use and carbon footprints by integrating a Hybrid Swarm Bipolar with Walk-SpreadAlgorithm with a unique Deformable Graph Convolutional Network (DGCN-SB-WSA)-aware virtual machine placement architecture. To ensGoogleent VM scheduling and management, real-world cloud workloads are analyzedusing the Google Cluster Dataset (GCD). The Deformable Graph Convolutional Network (DGCN) dynamically modelscloud infrastructure as a graph that captures intricate relationships among PMs and VMs, enabling adaptive placementchoices. The Hybrid Swarm Bipolar with Walk-Spread Algorithm (SB-WSA) then uses a dual-phase search approach tobalance local exploitation with global exploration, minimizing premature convergence and increasing performancewhile optimizing Virtual machine (VM) allocation. Comparing the proposed method to conventional VM placementtechniques, experimental results show that it dramatically 27 KW lowers power consumption, improves 98% resourceusage and 180 kg CO₂ decreases carbon emissions.
An electroencephalogram (EEG) is a critical diagnostic tool that monitors brain activity and detects epilepticseizures. The EEG signals, however, are complex and unwieldy, making it challenging to create an automated seizuredetection system. Additionally, sensitive healthcare data in clinical systems should be handled securely. The givenpaper describes an EEG-based seizure detection using blockchain security and transformer graph capsule networks (PTGG-CapsNet-Blockchain). This model integrates a superior signal processing system, machine learning, and blockchainto attain powerful seizure tracking and data protection. The structure takes EEG data of the CHB-MIT dataset, whichconsists of multichannel records of seizures in children. Preprocessing was performed using the Adjoint Bilateral Filter(ABF), which removes noise while preserving critical signal features. Using the Clifford Fourier Mellin Transform(CFAT), spatio-temporal features are extracted and thus capture the complex dynamics in EEG signals. Classificationwill be performed by a Pure Transformer-Based Gated Graph Attention Capsule Network (PT-GG-CapsNet),leveraging transformers' temporal analysis capabilities and graph capsule networks' modeling of spatial relationships.The Artificial Hummingbird Algorithm (AHA) will then be used for hyperparameter optimization, ensuring an optimalmodel. To achieve data security and transparency, the framework incorporates blockchain technology to allowdecentralized storage of EEG data. This guarantees integrity, immutability, and effective coexistence amongheterogeneous medical networks. The classification accuracy, precision, recall, and F1-score of the proposed model are99.7%, 99.5%, 99.2%, and 99.3%%, respectively, whichbeats baseline methods. This framework will offer real-worldclinical uses of blockchain that can operate in a heterogeneous medical setting, thanks to its low latency andinteroperability.
Spam emails continue to be a major issue in digital communication, hurting productivity and jeopardizingsecurity. This paper presents a real-time spam email filtering method that combines ensemble learning techniques(stacking and majority voting) with machine learning classifiers such as Logistic Regression (LR), Support VectorMachine (SVM), Stochastic Gradient Descent (SGD), and Decision Trees (DT). The objective is to improve the accuracyand reliability of spam detection systems. The proposed models were tested against Kaggle’s Email Spam Dataset oncrucial performance parameters such as accuracy, precision, recall, F-measure, false positive rate (FPR), and falsenegative rate (FNR). The results show that, while the stacking ensemble is competitive, the majority voting ensembleconsistently achieved good results. It delivers improved accuracy of 98.59%, precision of 98.59%, recall of 98.59%, andF-measure of 98.59% while keeping lower FPR of 0.019 and FNR of 0.009, making it the most dependable option forreal-world use. This study demonstrates the efficiency of ensemble learning, particularly majority voting, in buildingscalable and practical solutions for real-time spam email filtering. The findings lay a solid foundation for enhancingemail security mechanisms and user experience.
Malware detection is a significant factor in establishing effective cybersecurity in the face of constantly increasing cyber threats. This research article aims to investigate the field of machine learning (ML) techniques for malware detection. More specifically, the paper focuses on the Customized K-Nearest Neighbors (C-KNN) classifier and the Firefly Algorithm (FA). The work aims to assess the effectiveness of C-KNN and C-KNN with FA (C-KNN/FA) in malware identification using the MalMem-2022 dataset. The novelty of the proposed method lies in the synergistic integration of the C-KNN algorithm with the FA for metaheuristic optimization. The use of FA to select the most relevant features enables the C-KNN to train on a small and high-quality feature set. Therefore, the performance of malware detection will be improved. We compare the performance of both methods to understand the influence of KNN parameter adjustment and feature selection on malware classification. The C-KNN and C-KNN/FA have produced remarkable results in malware identification, reaching an accuracy of 99.98%. This accomplishment is quite encouraging. With regard to multiclass and binary classification methods, C-KNN and C-KNN/FA both perform better than their alternatives.
Precision agriculture relies on wireless sensor networks (WSNs) to support informed decision-making, thereby enhancing crop yields and resource management. A critical challenge in such networks is minimizing the energy consumption of sensor nodes while ensuring reliable data transmission. Sensor nodes are grouped using an optimal multi-objective clustering approach, which also chooses appropriate cluster heads (CH) for effective communication. By combining the exploration power of the Osprey Optimization Algorithm with the exploitation power of the Parrot Optimizer, a hybrid optimization approach improves CH selection. A hybrid deep learning framework, combining a convolutional autoencoder with a dual-key transformer network, is designed to monitor energy utilization and detect constraints affecting consumption. Training and testing performance of this framework is further improved using a metaheuristic based on the cooperative feeding and locomotion behavior of gooseneck barnacles. Experimental evaluation demonstrates superior performance, achieving 99.2% accuracy, 68 kbps throughput, 98% packet delivery ratio, and a network lifetime of 85 ms. With an average delay of 0.23 seconds, energy consumption is decreased to 39 J, demonstrating the effectiveness of the suggested strategy for dependable and sustainable precision agriculture applications.
Mobile Adhoc Networks are not without challenges such as limitations with scaling, and problems with excessive power consumptions which is mostly attributed to battery consumption. The movement of nodes causes an increasing use of energy, delay, and the shortened lifetime of nodes. A solution to these problems is a resource-efficient City Block-K Means Algorithm (CB-K Means) clustering method that prioritizes resource optimization and the lifespan of the network. These are localization, selection of Cluster Head (CH) and mobile node clustering. Mobile Node (MN) localization is performed using a Crossover and Mutation-based Jaya Optimization Algorithm (CM-JOA), followed by clustering through CB-K Means, where the CHs are selected with a linear scaling-based Satin Bowerbird Optimization Algorithm (LS-SBOA). The experimental results indicate that there is an improvement by 3.48% in the PDR (Packet Delivery Ratio), 7.3 percent packet loss, reduced clustering time of 3412ms and enhanced throughput of 889 bps.
In this research, we propose an integrated routing protocol termed Bacterial Foraging inspired Mamdani Fuzzy Inference based AODV (BF-MFI-AODV) for Vehicular Ad-hoc Networks (VANETs), which combines Mamdani Fuzzy Inference System (MFIS) and Bacterial Foraging Optimization (BFO) techniques. The protocol aims to address the challenges of dynamic and unpredictable network conditions in VANETs by leveraging fuzzy logic and bio-inspired optimization principles. BF-MFI-AODV enhances route discovery, maintenance, and optimization mechanisms, resulting in improved adaptability, reliability, and efficiency of communication. Through extensive simulations and real-world experiments, the performance of BF-MFI-AODV is evaluated in terms of packet delivery ratio, end-to-end delay, routing overhead, and network lifetime. Our results demonstrate the effectiveness of BF-MFI-AODV in enhancing the overall performance of VANETs compared to existing routing protocols. The proposed protocol shows promise in providing robust and efficient communication solutions for dynamic vehicular environments, thus contributing to the advancement of intelligent transportation systems.
Advanced intrusion detection systems are required due to the quick uptake of cloud computing and the growing complexity of cyber threats, especially Denial of Service and Distributed Denial of Service attacks. Deep learning architectures are becoming more popular because traditional IDS techniques frequently falter in dynamic, large-scale settings. Using datasets including CICIDS2017, NSL-KDD, and UNSW-NB15, this paper assesses the effectiveness of well-known DL architectures for intrusion detection, including Convolutional Neural Network, Recurrent Neural Networks, Long Short-Term Memory, and others. Key performance indicators such as accuracy, precision, and false positive rates are examined to compare the efficacy of these models. The findings show that some designs, like ResNet and Self-Organizing Map, perform well in structured environments but poorly on complicated datasets like KDDTest-21. Another important data gap highlighting the need for more research in this area is that most models do not automatically adjust to unexpected threats. This work aids in the creation of intelligent, scalable systems for changing network environments by evaluating the efficacy of DL-based IDS solutions.
