In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving techniques reduce the amount of energy consumed based on proper task scheduling approaches. The existing fuzzy-based hybrid genetic algorithm considers job length to optimize the makespan. However this fuzzy genetic encoded chromosome (fuzzy-GEC) algorithm considers both makespan and energy consumption to optimize the calculation of fitness value for assigning the task to the virtual machine (VM) by considering the characteristics of the task and VM. According to these characteristics, a fuzzy genetic rule-based encoding scheme is developed to schedule the tasks onto the virtual machines to minimize makespan and energy consumption. The effectiveness of the new technique is evaluated against FCFS and conventional genetic scheduling algorithm using Google Cloud trace workload. The results show the efficiency of the developed approach for makespan and energy consumption.

Memes have grown significance as social media has grown in popularity. Memes are challenging to categorize using conventional techniques since they are typically constructed using a combination of images and text, and their content is frequently hilarious or sarcastic. We suggest a deep learning-based method for classifying memes into a variety of categories, such as sexism, politics, and criticism. This highlights the necessity for a system that can evaluate memes automatically before they raise controversy or spread humor. Before judging the text as racist and abusive or not, it will first extract the text from the supplied image. If the language is found to be unacceptable, the third phase will further categorize the information into three categories: mildly racist and abusive, very racist and abusive, and hateful racist and abusive. The five thousand memes that made up the dataset for this study were divided into four categories: hateful racist and abusive, highly racist and abusive, slightly racist and abusive, and not racist and abusive at all. In current history, there has been an upsurge in concern over the propagation of inappropriate memes on social media. The proposed project provides a deep learning-based approach to categorizing objectionable memes using CNNs, RoBERTa, and BERT. We enhanced pre-trained algorithms using a dataset of racist and abusive and non-racist and abusive memes. Utilizing various assessment metrics, like accuracy, precision, recall, and F1-score, we assessed each model's performance.

India is a country with a rich tradition of agriculture, with approximately 70

The development of cloud technology has led to more resources being made available on demand. The recent spike in the cloud service demand requires further improvement of cloud-based data centers. As a result, effective task scheduling is necessary for cloud computing. To ensure equal load distribution to systems with increased scalability and performance, data centers must have a suitable task scheduling mechanism. An efficient task scheduling strategy tries to optimize output, decrease response time, use fewer resources, and conserve energy by matching the appropriate resources to the workload. The suggested technique employs a two-stage task scheduling approach. In the first stage, virtual machines are created by performing classification and clustering techniques based on historical task data, and in the second stage, a hybrid ant genetic algorithm is used to schedule the best VM for the task by combining the advantages of genetic algorithms with pheromone values from ant colony algorithms. The suggested approach accomplished cost-effective task scheduling with a short make-span.

accurate diagnosis of plant diseases is essential for reversing declines in crop output. To study plant illnesses, one must examine the outward symptoms produced by the plant in question. It is also essential for long-term husbandry that plant health be monitored and complaints about diseases made. Disease auditing in plants is tedious work that requires special equipment. It takes a huge commitment of time, courage when dealing with plant illnesses, and fortitude to endure the devilishly long processing time. As a result, landing the photos of the leaves and comparing it with the data sets is how image processing is employed for the detection of plant conditions. To learn to distinguish between photos of unhealthy and healthy leaves, a group of computers is trained on datasets of both types. In conclusion, we now have a straightforward method to descry the complaint existent in crops at a massive scale by employing machine learning to train on the vast data sets available privately. To aid greenhouse growers, this report has been written.

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

Change is the only word that does not change and the rest changes. Technology advancement has significant impact on the improvement of lifestyle in everyone’s lifetime. There have been numerous studies conducted related to understanding the effects of the Internet of things in the education. This paper reviews each study and their significant contribution on revealing the effect of each evolving methodology of the Internet of things on education. This study on IoT primarily lists out all the technology pertaining to the digital and smart devises being used by the students and teachers in the educational institutions. Some of them are, e-books, smart boards, voice command system, speech- to text-based note-taking systems, smartphones with educational applications, automated attendance recording, and AR-equipped systems. These solutions for education have given tremendous support to enhance the quality of education around the world. This study will help in understanding its impact through the research conducted with the results on these evolving methodologies of the Internet of Things for its slice of contribution in the education sectors.

In the competitive landscape of the job market, universities are faced with the challenge of not only providing quality education but also ensuring the successful placement of their graduates into the workforce. The use of advanced machine learning models offers a promising solution to this challenge by providing universities with the ability to efficiently analyse vast amounts of data and identify patterns that can be used to optimize their recruitment and placement strategies. In this paper, we delve into the exciting world of machine learning and explore how it can revolutionize university graduate employability. From predictive models that analyse student performance, interests, and career aspirations to identify the best-fit job opportunities for each student, to machine learning algorithms that match job candidates with suitable job openings based on their skills, experience, and qualifications, the possibilities are endless. It is essential for universities and companies to implement responsible machine learning practices and ensure that their algorithms are fair and unbiased to prevent issues of bias and discrimination. As the job market becomes more competitive and complex, universities and companies must leverage advanced technologies to remain competitive and attract top talent. By embracing machine learning and developing responsible machine learning practices, universities can optimize their recruitment and placement strategies and enhance the employability of their graduates. The use of advanced machine learning models presents an exciting opportunity for universities to optimize their recruitment and placement strategies and revolutionize university graduate employability.

The resources in cloud computing are provisioned to fulfill the application's computational requirements. The load balancer in a cloud data center assigns resources to virtual machines. However, the fault occurs when the server is under heavy load. As a result, resources must be adjusted. In this study, we are combining the support vector regression technique (SVRT) prediction model with resource scaling and migration. SVRT is used to predict future utilization of multi-attribute resources of a host. The approach is ideal for coping with nonlinear cloud resource workloads. After scaling the resources as required by the expected workload, the migration is in use to attain load balancing in the VMS. This paradigm for cloud computing infrastructures aims to improve system usage, lower costs and power consumption, and meet service level agreements (SLAs). We have evaluated the applicability of this framework using Google Cloud Trace.

Resource management is addressed using infrastructure as a service. On demand, the resource management module effectively manages available resources. Resource management in cloud resource provisioning is aided by the prediction of central processing unit (CPU) and memory utilization. Using a hybrid ARIMA–ANN model, this study forecasts future CPU and memory utilization. The range of values discovered is utilized to make predictions, which is useful for resource management. In the cloud traces, the ARIMA model detects linear components in the CPU and memory utilization patterns. For recognizing and magnifying nonlinear components in the traces, the artificial neural network (ANN) leverages the residuals derived from the ARIMA model. The resource utilization patterns are predicted using a combination of linear and nonlinear components. From the predicted and previous history values, the Savitzky–Golay filter finds a range of forecast values. Point value forecasting may not be the best method for predicting multi-step resource utilization in a cloud setting. The forecasting error can be decreased by introducing a range of values, and we employ as reported by Engelbrecht HA and van Greunen M (in: Network and Service Management (CNSM), 2015 11th International Conference, 2015) OER (over estimation rate) and UER (under estimation rate) to cope with the error produced by over or under estimation of CPU and memory utilization. The prediction accuracy is tested using statistical-based analysis using Google's 29-day trail and BitBrain (BB).

Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm’s consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation.