Pay-as-you-go models are used to grant users access to cloud services. While using the cloud, an imbalance workload on data centre resources degrades quality of service metrics like makespan, storage, high failure rate, and energy consumption. Hence proposed the heuristic based hybrid GA to enhance the QoS with resource allocation in cloud computing. The population is first initialized using the Binary encoding sorts the tasks according to priority. After that, the Best Fit algorithm compares the Best Fit with iterations of each fitness value depending on the computation time to shorten the make span. Heuristic crossover approach and mutation are then used to update the probability of the existing population with the new population lowers the failure rate by using the fitness value. Therefore, the proposed heuristic-based hybrid GA technique balanced the load and allocate the resources effectively to improve QoS performances. The outcome reveals that the proposed method of QoS performances attained less makespan, energy consumption, failure rate and execution time with effectively allocated the resources of 1% to 39% when compared to the previous methods in cloud computing.

This research delves into the utilization of machine learning algorithms to address the urgent challenge of assisting students in navigating a highly competitive job market. Recognizing the limitations of conventional methods in delivering effective guidance for securing job opportunities, there is a growing imperative to integrate advanced technology. Our model using Machine Learning (ML) algorithms offers customized solutions and emphasizes the algorithms that exhibit the highest effectiveness within this context. In the contemporary employment, achieving success extends beyond mere academic credentials, necessitating a holistic grasp of industry trends and in-demand skills. Through the application of machine learning, a fresh approach is presented, encompassing the gathering, and preprocessing of diverse data that encompasses skill proficiencies. This data forms the bedrock upon which ML algorithms operate, predicting and enhancing students’ likelihood of securing favorable job placements. The proposed work focuses on the careful selection of suitable machine learning algorithms, with special attention given to classification techniques such as Linear Regression, Random Forest, Decision Tree Classifier, K-nearest neighbors Classifier, and ensembled models. By meticulous evaluation and Ensemble Technique, these algorithms unearth intricate patterns within the data, deciphering the multifaceted factors influencing job placement outcomes. By deconstructing the performance of each algorithm, the report provides valuable insights into their strengths and potential synergies.

Despite extensive research efforts in Facial Expression Recognition (FER), achieving consistent performance across diverse datasets remains challenging. This challenge stems from variations in imaging conditions such as head pose, illumination, and background, as well as demographic factors like age, gender, and ethnicity. This paper introduces NIFER, a novel facial expression database designed to address this issue by enhancing racial diversity in existing datasets. NIFER comprises 3,455 images primarily featuring individuals with dark skin tones, collected in real-world settings. These images underwent preprocessing through face detection and histogram equalization before being categorized into five basic facial expressions using a deep learning model. Experiments conducted on both NIFER and FER-2013 datasets revealed a decrease in performance in multiracial FER compared to single-race FER, underscoring the importance of incorporating diverse racial representations in FER datasets to ensure accurate recognition across various ethnicities.

This project aims to enhance online experiences quality by giving users greater control over the content they encounter daily. The proposed solution is particularly valuable for parents seeking to safeguard their children, educational institutions striving to foster a more conducive learning environment, and individuals prioritising ethical internet usage. It also supports users who wish to limit their exposure to misinformation, including fake news, propaganda, and disinformation. Through the implementation of a browser extension, this system will contribute to a safer internet, reducing users' vulnerability to harmful content and promoting a more positive and productive online environment. The primary objective of this work is to develop a browser extension that automatically detects and censors inappropriate text and images on web pages using artificial intelligence (AI) technologies. The extension will enable users to personalise censorship settings, including the ability to define custom prohibited words and toggle the filtering of text and images. Accuracy estimates for various classifiers such as Random Forest (0.879), Logistic Regression (0.904), Decision Tree (0.878), Naive Bayes (0.315), and KNN (0.832) were performed.

Healthcare IoT seeks to use technology to better patient care, optimize operational efficiency, and provide remote monitoring and management of health issues. Resource management is crucial in the context of Health Internet of Things (HIoT) since it enhances the performance of healthcare services. This research paper proposes a resource management model in healthcare Internet of Things (IoT) by using deep learning and bio-inspired algorithms. A deep learning model LSTM model is used to resource failure prediction and bio-inspired algorithms are used for resource allocation and load balancing. An accurate prediction of resource utilization and effective resource management algorithm will improve the overall performance of IoT services for Health care application. The proposed approach incorporates deep learning methods to identify and anticipate anomalies, enabling the proactive identification of future problems or resource failures and resource utilization. In addition, bio-inspired algorithms are used to dynamically distribute resources and optimize system performance in real-time. The efficacy of the proposed fault-tolerant method is proved by extensive simulations and performance tests. The experiment results show the improvement in performance parameters as compared to state-of-the-art resource management models

This medical image segmentation plays a fundamental role in the diagnosis of diseases related to the correct identification of internal structures and pathological regions in different imaging modalities. The conventional fuzzy-based segmentation approaches, though quite useful, still have some drawbacks regarding handling uncertainty, parameter optimization, and high accuracy of segmentation with diverse datasets. Because of these facts, it generally leads to poor segmentations, which can give less reliability to the clinical decisions. In addition, the paper is going to propose a model, FTra-UNet, with advanced segmentation of medical images by incorporating fuzzy logic and transformer-based deep learning. The model would take complete leverage of the strengths of FIS concerning the handling of uncertainties in segmentation. Besides, it integrates SSHOp optimization technique to fine-tune the weights learned by the model to ensure improvement in adaptability and precision. These integrated techniques ensure faster convergence rates and higher accuracy of segmentation compared to state-of-the-art traditional methods. The proposed FTra-UNet is tested on BRATS, CT lung, and dermoscopy image datasets and ensures exceptional results in segmentation accuracy, precision, and robustness. Experimental results confirm that FTra-UNet yields consistent, reliable segmentation outcomes from a practical clinical application perspective. The architecture and implementation of the model, with the uncertainty handled by FIS and the learning parameters optimization handled by the SSHOp method, increase the power of this model in segmenting medical images.

Alzheimer’s Disease (AD) is the neuro-degenerative dementia, where the precise and early recognition of AD is vital for timely treatment to reduce mortality rate. A new automated model is implemented in this work for early discovery of AD in the Magnetic Resonance Imaging (MRI) brain scans. Initially, the input brain scans are taken from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Further, the acquired raw brain scans are visually improved by employing the binary normalization technique. The denoised brain scans are fed to the pre-trained Convolutional Neural Network (CNN) named GoogleNet for feature extraction. Next, the extracted richer feature values are fed to the Long Short Term Memory (LSTM) network for classifying the brain scan as Normal Control (NC), Mild Cognitive Impairment (MCI) and AD. In this manuscript, a Honey Badger Optimization Algorithm (HBOA) technique is incorporated with the LSTM networks for hyper-parameters optimization, where this procedure helps in diminishing the LSTM network’s complexity and computational time. The experimental results conducted on the ADNI database underscore the HBOA-based LSTM network's effectiveness, showcasing a remarkable mean classification accuracy of 97.83% in multi-class classification. Moreover, the sensitivity of HBOA based LSTM for AD/NC is 96.73% which is high when compared to the existing methodologies such as SVM with radial basis kernel function and NCSINs. This performance surpasses that of other comparative models for AD detection, emphasizing the superior capabilities and potential of the proposed method in the early detection.

Consider a G=(V,E) and the function f :V -{0,1,2}. . Unguarded with regard to is defined as a node u with  f (u) = 0  that is not next to a node with 1 or 2 . The function f (V₀ ,V₁,V₂ ) fulfilling the condition, in which each node u for which f (u)=0 is adjoint to at least one node v to which f (v) =2 , is referred to be a Roman dominant function, known as RDF of a graph G . Roman domination number (RDN) of graph represented by r_R (G), is the bare lowest count of guards that must be employed in any RDF. In this paper, we introduce a new form for graph called Semi-Middle Graph for any given graph and we find the RDN for the Semi - Middle Graph of some specific graphs. In the field of networking, the concept of the Semi-Middle Graph can be applied to network topology optimization. Specifically, it can be used in the design of communication networks where each direct connection (edge) between two nodes (devices or routers) has an intermediary node that facilitates more efficient data routing or enhances fault tolerance. Usefulness of Roman Domination Number of a Semi-Middle Graph in Networking includes Network Coverage and Monitoring, Fault Tolerance and Redundancy, Optimal Placement of Relays and Routers, Load Balancing and Resource Allocation.

At present, education is a matter of global concern and it is the responsibility of all States to be able to provide the ideal conditions so that it is accessible to the entire population. As indicated by one of the objectives of the 2030 Agenda which seeks to guarantee inclusive and equitable education quality. Different studies indicate that instructional design models allow the creation of optimal educational environments for all people and that their correct application allows students to generate satisfactory learning, however, there is an important group of people who are not considered in the tests of these models making it is impossible to reach the goal of achieving the desired educational inclusion. Therefore, the stated objective is to demonstrate that people with some type of disability have not been considered to work and to validate the studies that use different models, approaches or techniques to develop virtual learning environments. The results are worrisome as they demonstrate that of the 90 scientific articles analyzed, only 4.44% have included topics related to disability and of the total sample of participants that total 11,732 people, only 42, that is, 0.36%, had some type of disability. This shows that we are very far from being able to meet the sustainable development goal that seeks to guarantee inclusive and equitable quality education. Based on the results achieved, it is intended to sensitize governments, educational institutions and teachers around the world to work responsibly to close the gap that marginalizes people with disabilities and build appropriate virtual learning environments that guarantee that everyone can access and learn in the best way.

The integration of chaos theory and orthogonal moments has gained significant traction in contemporary image analysis. This paper presents a novel approach to image encryption and decryption, leveraging a modified logistic chaotic map and discrete orthogonal moments. The coefficients derived from Charlier polynomials and the image function are utilized to obfuscate the plaintext image. Furthermore, to bolster security measures, the pixel values of the obfuscated image are shuffled employing a modified logistic chaotic map. The encryption key is constructed from the parameters of both the chaotic map and Charlier polynomials, enhancing the robustness of the encryption scheme. Extensive experimental validation is conducted to assess the security of the proposed image encryption algorithm. Results demonstrate a considerable deviation in pixel values following diffusion via Charlier moments’ coefficients. Statistical tests and comprehensive security analyses affirm the resilience of the proposed algorithm against data loss attacks. The experimental result with Pearson correlation coefficient is almost 0, key space is greater than 2^210, and  information entropy can reach 7.8404,  which establish its superior security posture relative to existing algorithms within the domain of image encryption. The findings underscore the efficacy and reliability of the proposed scheme, positioning it as a viable solution for safeguarding sensitive image data in various applications.