Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
Resume Screener System
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
Round Robin (RR) scheduling algorithm is a widely used scheduling algorithm in timesharing systems, as it is fair to all processes and is free of starvation. The performance of the Round Robin algorithm depends very much on the size of the time quantum selected. If the time quantum is too large, the performance of the algorithm would be similar to that of FCFS (First Come First Serve) scheduling. On the other hand, if the time quantum is too small, the number of context switches will be large. Therefore, it is necessary to have some idea about the optimum level of time quantum, so that the average waiting time and turnaround times, and the number of context switches are not too large. Several extensions to the round robin algorithm have been proposed in the literature to overcome these difficulties. In this study, the author has picked some of these extensions and tried comparing their effectiveness by means of some examples.
This paper describes and evaluates several global optimization issues of Artificial Neural Networks (ANN) and their applications. In this paper, the authors examine the properties of the feed-forward neural networks and the process of determining the appropriate network inputs and architecture, and built up a short-term gas load forecast system - the Tell Future system. This system performs very well for short-term gas load forecasting, which is built based on various Back- Propagation (BP) algorithms. The standard Back-Propagation (BP) algorithm for training feed-forward neural networks have proven robust even for difficult problems. In order to forecast the future load from the trained networks, the history loads, temperature, wind velocity, and calendar information should be used in addition to the predicted future temperature and wind velocity. Compared to other regression methods, the neural networks allow more flexible relationships between temperature, wind, calendar information and load pattern. Feed-forward neural networks can be used in many kinds of forecasting in different industrial areas. Similar models can be built to make electric load forecasting, daily water consumption forecasting, stock and markets forecasting, traffic flow and product sales forecasting.
In RSA (Rivest-Shamir-Adleman) cryptography, the basic factors are key length, calculation time, security, authentication and integrity. Generally, in public key cryptography, the key length and security is directly proportional to each other. Original RSA uses two prime numbers as input, which gives the modulus 'n'; encryption and decryption process depends on modulus 'n'. The attacker can easily break the 'n' into two factors of prime number and so to avoid this problem, the authors have used three large prime numbers, it will increase the brute force time to factorize 'n'. This paper mainly focuses on the number of prime numbers used, security and time.
Fruit fly algorithm is a novel perspicacious optimization algorithm predicated on the foraging comportment of the authentic fruit flies. Recently, an incipient Fruit Fly Optimization Algorithm (FOA) has been proposed to solve optimization quandaries. In order to find optimum solution for an optimization quandary, fine-tuned parameters are obtained as a result of manual test in the fruit fly algorithm. This study deals with enhancing the probing efficiency and greatly ameliorate the probing quality and also on an automated approach for finding the cognate parameter by utilizing a grid search algorithm. Also it provides better ecumenical probing ability, more expeditious convergence, and more precise convergence. The optimization of a sizably voluminous antenna array for maximum directivity, utilizing a modified fruit fly optimization algorithm with desultory search of two groups of swarm and adaptive fruit fly swarm population size.
Usage of social medias have increased now-a-days. User share personal information and images through social network. While maintaining the privacy has become a major problem and also duplication of images have reduced the capacity of the databases. The authors a two level framework for maintaining privacy and securing the photo share. Users who want to maintain privacy are usually provided with access control. The canny edge detection technique is used for the deduplication of images. It increases the storage space capacity of the database. Watermarking is used for every image that is shared on the website for the copyright protection and restricting the images on other websites.
