Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated Annealing (SA) algorithms to solve JSSP. The proposed algorithm is mainly based on the genetic algorithm. The reproduction phase of the genetic algorithm uses the tabu search to generate new population. Simulated annealing algorithm is used to speed up the genetic algorithm to get the solution by applying the simulated annealing test for all the population members. The proposed algorithm used many small but important features such as chromosome representation, effective genetic operators, and restricted neighbourhood strategies. The above features are used in the hybrid algorithm to solve several bench mark problems. Keywords

Routing in networks is the major issue, because the packets may be sent through different topologies. This issue is addressed with the help of the metaheuristic algorithm. In the proposed work the Genetic Algorithm (GA) is integrated with Tabu Search to schedule the packets effectively in computer networks. The initial path is generated by Genetic Algorithm and then it is optimized by Tabu Search (TS) Algorithm. The importance of integration is that, to reduce the size of routing table.

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The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

Problem statement: The problem of scheduling n jobs on m machines with each job having specific machine route has been researched over the decade. The Job Shop Scheduling (JSS) is one of the hardest combinatorial optimization problems. Each resource can process at most one job at a time. Approach: This study proposes a new approach to solve a Job Shop Scheduling problem with the help of integrating Genetic Algorithm (GA) and Tabu Search (TS). After an initial schedule is obtained the GA, the result is given as an input to TS to improve the status of the initial schedule. The objective of this study is to minimize the makespan, process time and the number of iterations. This approach achieves a better result with the help of efficient chromosome representation, powerful crossover strategies and neighborhood strategies. Results: This research resolves the allocation of operation to different machine and the sequence of operation based on machine sequence. Job Scheduling is the process of completing jobs over a time with allocation of shared resources. It is mainly used in manufacturing environment, in which the jobs are allocated to various machines. Jobs are the activities and a machine represents the resources. It is also used in transportation, services and grid scheduling. Conclusion/Recommendations: The result and performance of the proposed work is compared with the other conventional algorithm and it is also testing using standard benchmark problems

Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.

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