Effective resource allocation is crucial for optimizing performance and efficiency in big data processing environments. In this study, we propose a novel algorithm that integrates advanced optimization techniques, including swarm intelligence-based firefly algorithm and deep learning-based resource allocation, proactive load balancing mechanisms, and holistic resource management strategies to address the complex challenges of resource allocation in large-scale big data infrastructures. The proposed algorithm is evaluated across key performance metrics, including energy efficiency, resource utilization, and SLA compliance, and compared against existing approaches. Results demonstrate significant improvements in energy efficiency, with an average power consumption of 5410 watts, average CPU utilization of 10240.125 Hz, and average SLA violation of 0.033625. These findings highlight the algorithm's effectiveness in optimizing resource allocation and enhancing system performance.

Within the domain of network intrusion detection, this study conducts a comprehensive evaluation of machine learning models using both aggregated route data and the NSL-KDD dataset. The evaluation encompasses an array of quantitative and qualitative parameters, including crucial Quality of Service (QoS) metrics. A distinctive feature of this research is the application of the Firefly algorithm to fine-tune neural networks, thereby optimizing predictive capabilities. The study consistently highlights the superiority of the proposed Firefly-tuned Neural model, showcasing excellence in precision, recall, F-measure, accuracy, and QoS parameters. It also includes an approach that coordinates the K-means++ algorithm for careful data segmentation and fuzzy logic for the fine-grained assignment of membership degrees. The study highlights how important model selection is to cybersecurity and provides priceless information on the advantages of various algorithms. In conclusion, the amalgamation of route data clustering, Firefly algorithm optimization, and a comprehensive assessment of QoS parameters promises significant advancements in network intrusion detection systems. Future research should explore the real-world adaptability and scalability of these techniques, fortifying network security against evolving threats and intricate environments while simultaneously enhancing QoS.

Recent studies have opined that the integration of Industry 4.0 (I4.0) technologies positively influences the performance of manufacturing firms. However, research exploring the interaction between these technologies and the well-established Lean Manufacturing (LM) approach and their combined impact on various aspects of organizational performance is limited. This study aims to address this gap by employing structural equation modeling (SEM) to analyze data collected from 202 respondents from the Indian automotive industry. The hypothesized relationships for this study are tested by using IBM® Statistical Packages for the Social Sciences and Analysis of Moment Structure software. The results demonstrated that I4.0 has direct and significant positive effects on LM practices as well as on organizational performances. However, the indirect effect of I4.0 technologies on organizational performances through LM practices is stronger, indicating complementarity. The research findings provide both theoretical and managerial implications on how these combined effects impact organizational performance. These findings also emphasize that LM practices are not obsolete; on the contrary, they are vital for adopting I4.0 technologies in the industry.

In light of the escalating prevalence of digital image forgery facilitated by advanced editing tools and widespread sharing on online platforms, the demand for effective forgery detection techniques has surged. This research introduces an approach to digital image forgery detection, employing a multi-stage architecture involving ELA (Error Level Analysis), CNN (Convolutional Neural Networks), and XGBoost. The ELA technique is initially applied to identify tampered areas within an image, followed by CNN for feature extraction. The feature vectors are then fed into an XGBoost classifier, categorizing images as either authentic or forged. This multi-stage process works towards enhancing the detection accuracy and efficiency of forged image detection. The proposed algorithm achieved notable accuracy levels of 90.83%, 96.82%, and 82.82% on the CASIA v1, CASIA v2, and MISD datasets respectively.

Artificial Intelligence (AI) and Machine Learning techniques (ML) play a vital and significant role in finding the solutions to real life problems and making our life smooth. When this machine learning is used for the medical world in prediction models it will become very helpful for the experts in making their diagnostic decisions quickly and more accurately. Implementation of these advanced techniques will lead medical science to an improved version of its. In this comparative study six different learning techniques i.e. Decision Tree (DT), Support -Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF) and eXtreme Gradient Boost(XGB) are analyzed on the basis of some parameters like Time complexity, f-score, recall, accuracy and precision. In the experimental model, these techniques are implemented on various diseases like Heart disease, Breast cancer and Obesity diagnosis. The overall dataset is divided into 80:20 ratios as training and testing datasets. The results obtained from the given setup show that the DT will give the optimized results for all diseases while the time complexity is optimized for KNN and XGB.

Network traffic and load are only expected to increase. Data is lost while being sent between nodes and devices as a result of traffic congestion at bottlenecks. WLANs are becoming the go-to technology for access in hotspot locations, workplaces, and residences. A station in a wireless local area network may be able to connect to several access points (APs). So, deciding which AP is "optimum" from a selection of potential candidates is a pertinent question. The user merely associates the AP with the strongest received signal strength while using IEEE 802.11. But this could lead to a substantial load imbalance among a number of APs. With only a minor modification to the load distribution mechanism, adopting load balancing can improve efficiency and utilization in addition to reducing processing delays. To increase the effectiveness of the WLAN, we provide a dynamic load balancing strategy in this paper. The network and user overhead will be reduced by the suggested dynamic strategy. The suggested method utilizes network load and throughput as performance criteria for performing load balancing. The method's viability is checked using Riverbed Modeller Simulator. The experimental findings validate the effectiveness of the proposed technique in enhancing network throughput and reducing access point congestion.

In order to improve the calculation efficiency and reduce the uncertainty in the reliability evaluation process of flat car underframe, a new method based on an improved genetic simulated annealing algorithm-back propagation neural network (GSA-BP) and probability box is proposed. Firstly, a certain type of flat car underframe is taken as the research object, genetic evolution algorithm (GA) and simulated annealing algorithm (SA) are utilized to optimize the weights and thresholds of the GSA-BP neural network and obtain the best initial parameter values. Applying the values, the BP neural network is trained, and the improved GSA-BP neural network is established which is verified by test functions. Secondly, the central plate sub-model of the flat car underframe is obtained by sub-model technology and the correctness of its boundary conditions is tested. On this basis, the static strength analysis of the sub-model is carried out. Finally, the improved GSA-BP neural network and probability box are utilized to evaluate the reliability of the sub-model of flat car underframe, and the Monte Carlo method is utilized to verify it. The results show that the proposed method not only improves the accuracy of reliability evaluation but also shortens the calculation time from 10 hours to 6 hours. The calculation efficiency is increased by 40%, which further verifies its superiority and feasibility in structural reliability evaluation.