Manoj Limchand Bangare

Scheduling refers to the process of allocating cloud resources to several users according to a schedule that has been established in advance. It is not possible to get acceptable performance in settings that are distributed without proper planning for simultaneous processes. When developing productive schedules in the cloud, it is necessary for work scheduling to take a variety of constraints and goals into consideration.When dealing with activities that have performance optimization limits, resource allocation is a very important aspect to consider. When it comes to cloud computing, the only way to achieve great performance, high profits, high scalability, efficient provisioning, and cost savings is with an exceptional task scheduling system. This article presents a grey wolf optimization (GWO) based framework for efficient task scheduling in cloud computing environment. The proposed algorithm is compared with particle swarm optimization (PSO) and flower pollination algorithm (FPA) and GWO is performing task scheduling in less execution time and cost in comparison with PSO and FPA techniques. Execution time taken by GWO to finish 200 task in 120.2 ms. It is less than the time taken by PSO and FPA algorithm to finish same number of tasks.

In the age of the Internet of Things (IoT), an immense volume of data is rapidly generated and entrusted to external units, including cloud centers for storage and analysis. This implies that the data produced by IoT devices is conveyed and stored in a distant location, where it can undergo processing, examination, and utilization to extract valuable insights and facilitate informed decision-making. Privacy protection methods can be applied in IoT settings to safeguard sensitive and private information from potential security risks. Within the scope of IoT privacy, data sanitization involves the permanent and irreversible concealment of all sensitive data from extensive streams of information. Conversely, data restoration is the reverse process of data sanitization. Considering privacy preservation in IoT for data streaming, this research paper implements a Deep Learning (DL) tuned key-based data sanitization and restoration approach. The entire process of the implemented approach is handled by two significant steps such as data sanitization and data restoration. In the data sanitization step, three processes take place such as retrieval of features, key generation via Self-Adaptive Osprey Optimization (SAOO) and key tuning via Enhanced Convolutional Neural Network (ECNN). Initially, features such as raw features, statistical features and improved entropy-based features are retrieved (considering this as sensitive information. To sanitize this, key generation is the subsequent phase, where optimal keys are generated by SAOO and its security and confidentiality are ensured by the Khatri–Rao product. Then, these optimal keys are tuned by ECNN to ensure the efficacy of privacy preservation in IoT without compromising the performance of the model. Finally, the data restoration process is to recover the previously sanitized data in an IoT environment and reconstitute the original data from their sanitized form while maintaining the security and confidentiality of raw sensitive data. With these steps, the privacy of an IoT environment is protected from any form of issues. Additionally, this approach is experimentally explored to determine its efficacy using various analyses.

Epilepsy is a neurological condition resulting in irregular activity in the brain’s neurons, which leads to frequent unexpected seizure occurrences. Hence, it is essential to predict this disorder at an early stage that prevents the patient’s life. Various Deep Learning (DL)-based techniques are employed in this aspect. Still, the models rely on inaccurate performance with improper training. In this work, a hybrid classification system consisting of SqueezeNet and Improved Long Short-Term Memory (ILSTM) models is proposed to predict epileptic seizures using Electroencephalography (EEG) inputs. The prediction of seizure involves three stages. The EEG signal initially undergoes preprocessing, where the improved Kalman filter is employed to preprocess the signal. After that, several features such as Empirical Mode Decomposition (EMD), improved Stockwell transform, spectral density, spectral skewness, and statistical features are derived from the preprocessed signal. The extracted features are subjected as the input to the prediction stage with a hybrid classification system utilized. The hybrid system is the integration of ILSTM and SqueezeNet classifiers. Additionally, the performance of the proposed method is compared to that of conventional models using a range of performance metrics. As a result, the suggested model shows superior performance compared to the existing techniques.

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.

The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.

In cloud computing, load balancing is crucial for effective resource management. Keeping servers from getting overworked entails dividing up incoming network traffic or computational tasks among several servers. Better resource management, increased throughput, and quicker reaction times result from this. Several heuristic and metaheuristic techniques have been used to disperse the load across the available virtual machines. Researchers have worked very hard to find a solution for the load balancing issue. This paper uses the following stages to develop a novel load-balancing model with optimization assistance: Virtual Machine (VM) classification, load balancing, and replica management are the three main processes. For the VM classification process, a modified version of the fuzzy clustering approach is suggested. For the load balancing procedure, the COOT Insisted Bald Eagle Search (COOTIBES) model is suggested. This optimization-assisted load balancing takes into account a number of limitations, including frequency, makespan, memory usage, resource utilization, and execution time. Additionally, the suggested COOTIBES algorithm manages replicas while taking load, put cost, and storage cost into account. Lastly, using various performance indicators, the suggested work’s performance is contrasted with traditional models. While the Inquisitive Genetic Algorithm with Grey Wolf Optimization Algorithm (IG-GWO), Ant Colony Optimization for Continuous Domains (ACOR), Life Choice-Based Optimizer (LCBO), Cat Swarm Optimization (CSO), Genetic Algorithm Combined with First Come First Serve + Genetic Algorithm Combined with Round Robin (GA-FCFS[ ][ ][ ]GA-RR), Jellyfish Optimization (JFO), and Namib Beetle Optimization (NBO) offer bigger makespan values, the COOTIBES scheme gives a smaller makespan of 180 for a task count of 2000.

Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

This paper investigates the crucial field of magnetic resonance imaging (MRI) and convolution neural networks (CNN) for brain tumor identification. A brain tumor is an aberrant cell proliferation that disrupts normal brain function and may pose serious health risks. Leveraging the power of CNN, our approach seeks to revolutionize the identification process through the analysis of MRI images. The CNN acts as a sophisticated interpreter, meticulously scrutinizing intricate patterns within brain scans to discern subtle abnormalities indicative of tumors. By training the CNN on diverse MRI datasets, our method enhances accuracy in distinguishing between healthy and pathological neural structures. This research signifies a pivotal advancement in medical diagnostics, showcasing how CNN-driven analysis of MRI images contributes to early and precise detection of brain tumors, offering improved prospects for patient care and treatment outcomes

Food instability has been linked to infertility, health issues, accelerated aging, incorrect insulin regulation, and more. Innovative approaches increased food availability and quality. Agriculture environment monitoring systems need IoT and machine learning. IoT sensors provide all necessary data for agriculture production forecast, fertilizer management, smart irrigation, crop monitoring, crop disease diagnosis, and pest control. Precision agriculture may boost crop yields by prescribing the right water-fertilizer-paste ratio. This article presents IOT based fertilizer recommendation system for Smart agriculture. This framework uses IoT devices and sensors to acquire agriculture-related data, and then machine learning is applied to suggest fertilizer in the correct quantity and at the appropriate time. The data acquisition phase collects input data, including soil temperature, moisture, humidity, regions' weather data, and crop details. Features are selected using the Sequential Forward Floating Selection algorithm. Multilinear Regression performs data classification. The performance of SFSS-MLR is compared to Random Forest, C4.5, Naïve Bayes algorithm. SFSS MLR is better in accuracy, precision, recall and F1. The accuracy of SFSS MLR is 99.3 percent.

Improved Reliability and Low Latency Communication (IRLC) with Augmented Reality (AR) has become an emerging technology in today’s world. To minimize an accessory adaptation for Customer Equipment (CE) in AR, it may be feasible to offload the AR workload onto the onboard devices. Mobile-Edge Computation (MEC) will improve the throughput of a CE. MEC has caused enormous overhead or communication omissions on wireless media, making it difficult to choose the optimal payload proposition. The proposed system explores on-board devices that work together to achieve an AR goal. Code splitting is a Bayesian network used to examine the overall interdependence of efforts. From a longevity and endurance perspective, it is used to reduce the Probability of Supplier Failure (PSF) of an MEC-enabled AR environment. Weighed Particle Swarm Optimization (WPSO) was proposed despite the reality based on the emphasis on balancing the issue. As a result, a heuristic-based WPSO facilitates to improve the performance measures. A hybrid method could significantly increase the assertion of a predicted PSF in various network scenarios compared to the existing communication technologies. A preliminary iterative approach is suitable for AR operations and IRLC scenarios to generalize the attributes.