Wireless Sensor Networks (WSNs) are vital in applications like environmental monitoring, smart homes, and battlefield surveillance. Comprising small devices with limited resources, WSNs require efficient node deployment for power optimization and prolonged network lifetime, ensuring sufficient coverage and connectivity. This study introduces an Intelligent Satin Bower Bird Optimizer augmented with reinforcement learning (ISBO-RL), enhancing coverage and connectivity. ISBO-RL focuses on optimal sensor placement for improved coverage and connectivity, using an Optimum Position Finding (OPF) method to identify key sensor node locations. Reinforcement learning is integrated into the ISBO algorithm, allowing nodes to adapt based on performance and changing conditions. Experimental results on diverse platforms highlight ISBO-RL’s efficacy and its superior coverage and connectivity performance as compared to other algorithms. ISBO-RL represents a significant advancement in the field of Wireless Sensor Networks, offering a promising solution to address the challenges of efficient node deployment and network optimization in various critical applications.

Over the years, Cloud computing has significantly influenced various sectors, including Web learning, as education is considered crucial for individual and national growth. The primary objective is to develop an Application Model that supports Web learning Services, addressing the limitations of existing web learning systems in terms of infrastructure and effectiveness. Cloud technology provides a platform to host web learning applications on a service basis, accessible to end users through the internet via cloud infrastructure. This approach offers cost-effective packages for educational organizations, benefiting both trainers and learners. To achieve this objective, a combination of technologies is required. The paper further emphasizes the importance of Web learning Design features and analyzes the significance of cloud computing. It explores the implementation of a cloudbased application model for web learning systems, focusing on its working methodology, architecture design, development tools, and external interfaces. Additionally, it explores the use of software engineering approaches in this context. The paper highlights the importance of utilizing cloud environments for institutes and learners, emphasizing the potential benefits and opportunities in terms of design.

Abstract Hyperspectral remote sensing images obtained from cameras are characterized by high-dimensions and low quality, which makes them unfavorable for various analytics purposes. This is due to the presence of visible and invisible frequencies of the reflected light making it poorly reveal the spectral signatures of the image. Visual communication advancement has paved the need for Image Super-Resolution (SR) which recovers high-resolution images from low-resolution images. Several works were carried out earlier on image SR using variants of supervised and unsupervised models that still lack accuracy. In this paper, we propose an unsupervised learning model titled Gompertz Function–based Convergence War Accelerometric Optimization–GAN framework for generating of High-Resolution (HR) images. The framework comprises a pre-processing stage, where the incoming Low-Resolution (LR) image is preprocessed for noise removal by applying Shannon-Gaussian Filter (S-GF). Following is the Gradient Domain Approach based Tone-Mapping (TM). Skew correction is done to remove distortion and maintain original resolution that may change during TM stage. The next stage comprises the boundary and edge enhancement of the resulting preprocessed image generated by the method of Inverse Gradient Mapping (IGM) followed by patch extraction to extract minute low-frequency information from the resulting boundary and edge-enhanced image. The contrast of the enhanced patches is improved by removing blurriness effect. The preprocessed image patches are then fed into the Gompertz Function-based Convergence War Accelerometric Optimization – GAN for feature mapping on the trained SR Image features that are clustered using Krzanowski and Li- Kantorovich Metric-K-Means clustering Algorithm (KL-KM-KMA) for effective generation of SR image. The developed model is validated for both qualitative and quantitative measurements. Comparisons are made with several other state-of -the-art methods for accuracy of 98.05%, precision of 97.98%, inception score of 8.71, Fréchet Inception Distance of 36.4 with reduced clustering and training time proving the efficiency of the proposed model.

Fog computing refers to the operations performed in a distributed network of nodes on the edge of the network to provide faster output generation for emergent requests.The fog layer brings computation closer to the devices, thereby reducing latency in the network.However, in recent years, fog computing has been subjected to several problems, including load balancing.Load balancing ensures appropriate allocation and distribution of resources and workload in a fog network.That being said, it's important to note that the nature of nodes in a fog network can be heterogeneous.In such a situation, it's crucial to have a load balancing mechanism that routes requests to the appropriate node based on the type of the requests, the load on the nodes, and the total load on the system.One way to solve the issue would be applying the conventional load balancing algorithms, but traditional load balancing schemes don't apply here since they tend to work amongst homogeneous sets of nodes with similar resources.In the proposed research, three load balancing schemes, namely-Highest Capacity Mode, Earliest Predicted Response Time, Equal Capacity Mode have been proposed.Finally, an optimal approach, a hybrid algorithm, and taps on the benefits of the 3 types of load balancing schemes have been proposed with the best performance among all the schemes.

The conventional method of categorizing cervical cancer types relies heavily on the expertise of pathologists, which is associated with a lower degree of precision. The utilization of colposcopy is an essential element in the prevention of cervical cancer. Colposcopy has been a crucial component in the reduction of cervical cancer frequency and humanity rates over the past five decades, in conjunction with precancer screening and treatment. The rise in workload has resulted in reduced diagnostic efficiency and misdiagnosis during vision screening. The utilization of the convolutional neural network (CNN) model in medical image processing has demonstrated its superior performance in the cervical cancer type within the realm of cavernous learning. The present study puts forth two convolutional neural network architectures based on deep learning for the identification of cervical cancer through the analysis of colposcopy images. The models employed in this research are VGG19 (TL) and Colposcopy Ensemble Network (CYENET). The utilization of VGG19 as a transfer learning approach has been implemented in the CNN architecture for research purposes. The Colposcopy Ensemble Network has been developed as a novel model for the automatic classification of cervical cancers from colposcopy images. The model's precision, selectivity, and responsiveness are evaluated. The VGG19 model exhibited a classification accuracy of 70.3%. The outcomes for VGG19 (TL) are moderately satisfactory. The kappa score analysis of the VGG-19 perfect inferred that the model falls within the moderate classification category. The findings of the experiment indicate that the CYENET model demonstrated noteworthy levels of sensitivity, specificity, and kappa scores, specifically, 90.4%, 95.2%, and 88%, correspondingly. The CYENET model exhibits an enhanced classification accuracy of 90.1%, surpassing the VGG19 (TL) model by 10%.

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications.

The transportation infrastructure of the future will be based on autonomous vehicles. When it comes to transportation, both emerging and established nations are keen on perfecting systems based on autonomous vehicles. Transportation authorities in the United States report that driver error accounts for over 60% of all accidents each year. Almost everywhere in the world is the same. Since the idea of self-driving cars involves a fusion of hardware and software. Despite the rapid expansion of the software business and the widespread adoption of cutting-edge technologies like AI, ML, Data Science, Big Data, etc. However, the identification of natural disasters and the exchange of data between vehicles present the greatest hurdle to the development of autonomous vehicles. The suggested study primarily focused on data cleansing from the cars, allowing for seamless interaction amongst autonomous vehicles. This study's overarching goal is to look at creating a novel kind of Support Vector Machine kernel specifically for P2P networks. To meet the kernel constraints of Mercer's theorem, a newly proposed W-SVM (Weighted-SVM) kernel was produced by using an appropriately converted weight vector derived through hybrid optimization. Given the advantages of both the Grey Wolf Optimizer (GWO) and the Elephant Herding Optimisation (EHO), combining them for hybridization would be fantastic. Combining the GWO algorithm with the EHO algorithm increases its convergence speed, as well as its exploitation and exploration performances. Therefore, a new hybrid optimization approach is proposed in this study for selecting weights in SVM optimally. When compared to other machine learning methods, the suggested model is shown to be superior in its ability to handle such issues and to produce optimal solutions.

Numerous apps utilize dependable start to finish TCP as a vehicle convention because of down to earth contemplations. There are a wide range of TCP variations generally being used, and every variation utilizes a particular clog control calculation to evade blockage, while too endeavoring to share the basic system limit similarly among the contending clients. This paper appears how a middle of the road hub (for example system administrator) can recognize transmission condition of the Transmission control protocol customer related with a Transmission control protocol stream inactively observing the traffic. A vigorous, versatile & nonexclusive AI based strategy which might be of enthusiasm for organize administrators that tentatively induces CNWD and the hidden variation of misfortune-based TCP calculations inside a stream from inactive traffic estimations gathered at a middle hub. The strategy can likewise be stretched out to anticipate other TCP transmission conditions of the customer. We accept that our investigation additionally has a possible advantage and open door for analysts and researchers in the systems administration network from both scholarly world and industry who need to survey the attributes of TCP transmission states identified with organize blockage. We approve the heartiness and adaptability approach of our expectation model through countless controlled tests. In this way, the precision of trial gives copied arrange, practical and joined situation settings and over numerous TCP blockage control variations show that our model is sensibly compelling and has impressive potential.

Multilayer Social networks are an important part of human life to interact on different networks at the same time. Due to the openness of such networks, they become a platform for spammers to spread malicious behaviors. Hence, there is an urgent need for effective detection of malicious behaviors; thereby, enabling the networks to take mitigation actions to decrease the possibility to reward such activities. Detection of suspicious behaviors in previous works is challenging due to the problems of community detection, the large amount of feature corruption, and memory requirements. Thus, to deal with such problems, in this paper, an efficient clustering-based detection of malicious users in multilayer social networks is proposed. Initially, the input dataset is pre-processed and used for Exponential Distribution based Erdős–Rényi based graph construction. From the graph structure, two types of data, such as user representations and graph features are extracted for graph encoding using the Soft Sign activated Graph Auto Encoder model. Then, the decoding is done to predict the information diffusion level, thereby, ranking the users using the Laplace Regularization technique. Then, the ranked users are clustered into different groups using the Pareto Front based K-Means Clustering Algorithm technique. Finally, the experimental results were analyzed to demonstrate the efficacy of the proposed model to detect malicious users in multilayer social networks.

All around the globe, the waste age rates are rising step by step. Overseeing litters appropriately is fundamental for building maintainable and decent conditions. Be that as it may, it has stayed Trash assortment has gotten hard to keep up or oversee by the individuals in their everyday life. Compelling trash the executives, less on city making plan. Regularly, unwanted things are arranged or are transparently scorched. Such practices have made add to worldwide atmosphere changes, serves a reproducing ground for ailment vectors, and furthermore elevates to urban viciousness. To beat these issues, we are building up an Autonomous Garbage Collecting Robot. This Robot is utilized to gather the trash from the containers. At the point when the containers are filled to a specific level, that receptacle sends a notice to the robot. Presently this robot is utilized to gather the trash from that specific receptacle and dumps the trash.

DevOps is a collaboration of development team and the operational team working together towards developing a software from scratch to until the software is fully accomplished. Lately software products were built using primitive models like waterfall model. DevOps was introduced for faster delivery of the products, as in comparison to the models developed lately as well as developing foolproof software applications. DevOps provides a sound working environment, as well as promoting innovation and changes in the software developed. DevOps aims at building steady and fast operating working software. Some of the scenarios where DevOps lifecycle is used in resolving problems. DevOps promotes transparency among different teams of an organization, simplification of business processes. DevOps makes the process simplified by reducing complex tasks to simpler tasks, faster release cycles. DevOps promotes faster release cycles along with continuous improvement. Motivation behind using DevOps is to reduce the cost of delivery, release quality, increase release velocity, reduce mean time. DevOps lifecycle increases agility, shortens releases, improves reliability and is highly scalable. DevOps plays a major role in building software from the starting phase of requirements gathering to the final stage as well as the maintenance and updating versions once the product is out in the market for use.

With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced that outperforms the conventional k-means clustering for image retrieval based on vision. The study emphasises how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be improved in conjunction with quantitative analysis. Within the field of DCE-MRI, ACICA stands out as a unique intravascular attention measurer. Utilising the spatial independence of extravascular and intravascular magnetic resonance imaging (MR) data, ACICA offers a strong foundation for DCE-MRI image analysis. It incorporates pharmacokinetic modelling to optimise the time lag, especially useful for arterial curves, and a reference region (RR)-based technique to adjust the intravascular concentration curve. The model's evaluation yields outstanding results, with recall and accuracy ranging from 83 to 99

In today's internet-driven world, a multitude of attacks occurs daily, propelled by a vast user base. The effective detection of these numerous attacks is a growing area of research, primarily accomplished through intrusion detection systems (IDS). IDS are vital for monitoring network traffic to identify malicious activities, such as Denial of Service, Probe, Remote-to-Local, and User-to-Root attacks. Our research focused on evaluating different auto-encoders for enhancing network intrusion detection. The proposed method sparse deep denoising auto-encoder approach produces the dimensionality reduction used to predict and classify attacks in datasets. With the most records among the datasets by training the auto-encoder on normal network data, this utilized reconstruction error as an indicator of anomalies. We tested our approach using standard datasets like KDDCup99, NSL-KDD, UNSW-NB15, and NMITIDS. Remarkably, our sparse deep denoising auto-encoder achieved an accuracy of over 96% based solely on reconstruction error. The primary aim of this work is to improve intrusion detection by achieving higher detection accuracy compared to existing methods.

In the Internet of Things, Wireless Sensor Networks (WSNs) are networks of interconnected sensors that wirelessly collect and transmit information about the environment. Using IoT sensors, IoT applications can remotely monitor and control physical environments. Clustering in WSNs involves organizing sensor nodes into groups called clusters with one or more CHs for efficient data integration, communication and management, improving network performance and resource utilization. In WSNs, achieving energy efficiency is critical to extend network lifetime and ensure stable operation. An important aspect contributing to energy optimization is the selection of CHs. However, the lack of an efficient cluster head selection mechanism remains a significant challenge. Therefore, this study introduces an optimized multivariate cluster head selection method that leverages the Improved Golden Eagle Optimization Algorithm (IGEOA). With this approach, the selection of CHs is optimized, combining multiple objective functions designed for energy efficiency. By using this algorithm, clusters are formed based on the selected CHs. In addition, a cluster maintenance phase is integrated to supervise the post-establishment clustering of the network, which ensures optimal cluster performance and resource utilization in WSN. Evaluation through simulation illustrates that the proposed method significantly improves both performance and energy efficiency in a WSN environment.

Restoring Moiré images presents significant challenges due to the complex interference patterns that obscure image details. These patterns often degrade the quality of images, making accurate restoration crucial for various applications. Effective Moiré image restoration requires advanced techniques to overcome the difficulties posed by these intricate artifacts. This study introduces an Adaptive Residual Convolutional Neural Network (RCNN) for Moiré image restoration, augmented with the Zebra Optimization Algorithm (ZOA) to enhance both feature extraction and restoration accuracy. The hybrid model leverages ZOA's capability to balance exploration and exploitation, optimizing network parameters to improve learning efficiency. Tested on the DIV2K dataset, this approach demonstrates significant improvements in handling complex Moiré patterns, as evidenced by enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values.

The proposed research article focuses on Continuous Integration, Testing, Delivery and Deployment in terms of DevOps environment. DevOps is a combination of development and operations. In a usual work environment, both development and operations of that company work entirely differently where mutual interaction is said to be minimum. Thus, to overcome that, DevOps was introduced, where both development and operations must work together in the same environment. In this article we will discuss what each of these terms means, how they work, why they are important in today’s software development life cycle (SDLC), what tools are used for each of them, etc. We will consider a few scenarios where the above-mentioned topics are better than the traditional or the most widely used SDLC methodologies.

The paper investigates the use of "Shree Rama Prashanavali," a Vedic method employed in Indian Astrology for the encryption or decryption of Hindi characters. "Shree Rama Prashanavali" is taken from the "Shree Ramacharitramanas" one of the significant works of the great Saint Tulsidas. It is used to infer the most probable solutions to the problems/issues by believers. The "Shree Rama Prashanavali" embeds 9 philosophical couplets/verses which are again taken from "Shree Ramacharitramanas" in a matrix form based on which the solutions to the problems/issues are decoded. The devised model is used as a Multilingual model to interpret the basic model of "Shree Rama Prashanavali" into multiple languages so that people from different regions can understand and use this model to get their answers. The possibility to include voice recognition and a numerical pad for disabled people and also to translate into more different languages for people living in other parts of the world is there.

Recent advances in research on the Multi-agent System (MAS) optimal control issue will help sectors like robotics, communications, and power systems. This work looks at the intelligent design of a large-scale multi-pursuer and multi-evader pursuit-evasion game. Based on reinforcement learning, a distributed cooperative pursuit method with communication is created. The famed Curse of Dimensionality poses a serious danger to multi-player pursuit-evasion game designs due to the sheer number of agents, especially in hostile areas where there aren't many communication options available to encourage player information exchange. In order to find the best pursuit-evasion strategies using a novel type of probability density function (PDF) rather than exhaustive data from all the remaining teams or agents, the Mean Field Games (MFG) theory has been used. A novel MAS optimum type oversight system with a decentralised and computer-friendly decision method is urgently needed. Mean field game theory is used to create the Actor-critic-mass (ACM), a decentralised optimal control system, to address the aforementioned issues. Additionally, the homogeneous decentralised Actor-critic-mass (HDACM) which improves the ACM method, does away with restrictions like homogeneous agents and cost functions. Finally, two applications make use of the PAS algorithm.

The proposed work highlights the importance of testing in machine-learning applications and the ensuing need to increase model quality to decrease the likelihood of errors. The proposed work considers patient health information that may be utilized for decision-making or prediction utilizing various computations, and in this instance, it emphasizes the development of artificial neural networks with the multilayer perceptron method to forecast cardiac abnormalities. The dataset employed in this work includes information about the patient’s demographics, clinical measures, and medical background. Utilizing this labelled dataset, one may estimate if each patient has heart disease or not. We developed a GUI to collect data about a new patient to accomplish the task specified. After the model is generated, effective functionality testing was performed on the model. As a result of the testing report’s assistance in identifying faults and defects, the artificial neural network’s accuracy ultimately improved as a result of its improved accuracy following correction. After undergoing feature testing, the application’s accuracy ascended to 96.69%, which is a higher percentage than was attained using any alternative method.

The wireless sensor networks (WSN) provides advancement of number of revolutionary applications such as localization, target tracking, etc. Most of these applications involve a numerous sensor device that are connected to the base station which behaves as a gateway to connect internally and cloud computing environments. The key operation of WSNs is data collection, data sensing and transmission. However, the sensor devices gather data and is communicated over the intermediate node in an episodic manner for smart decisions periodically. Enhancing the tracking prediction accuracy, reliability of network and lifetime performance for data gathered is the important objective of target tracking applications using WSNs. This work presents Reliable Target Tracking (RTT) model employing WSNs. First, in achieving higher prediction accuracy a Modified Kalman Filter (MKF) is introduced. Second, improved cluster head (CH) selection and multi-objective-based route optimization are presented. Experiment results shows the RTT model achieves major outcome when compared with present target tracking model employing WSNs for improving energy efficiency, tracking accuracy, latency reduction and communication overhead.