Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code
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
The concept of frequency reuse was a significant innovation in cellular radio networks, since it allowed for the resolution of two fundamental issues: spectrum congestion and user capacity, in particular. This approach is based on the intelligent allocation of channels as well as the reuse of such channels. The radio channels available to each cellular base station are assigned to a specific geographic area, referred to as a “Cell”, for usage by the base station. Hexagonal cells are preferred because they have a large surface area and have a radiation pattern that is quite similar to a circular one. The term "cluster" refers to the number of cells that collectively make use of the whole range of available frequencies. In this study cellular system with different cluster sizes is demonstrated using the ‘C’ programming language to demonstrate how to locate co-channel cells in a cellular system (program).
In today's world, internet evaluations are critical in influencing customer purchasing habits and improving worldwide communications among consumers. Internet giants like Amazon and Flipkart provide customers an opportunity to share their experiences and thoughts about the product's performance with respective customers. Classification of reviews into positive and negative sentiment is needed in order to obtain useful information from a huge number of reviews. When it comes to extracting subjective information from texts, Sentiment Analysis utilises computer algorithms. One of the most important NLP (Natural Language Processing) jobs is sentiment analysis, often known as opinion mining. The field of sentiment analysis have got attraction. It is one of the basic issues of sentiment analysis to solve the problem of sentiment polarity classification. A generic sentiment polarity classification method is provided along with comprehensive process explanations. This study's data comes from online product evaluations gained from sites like Amazon, Flipkart, eBay, and others that influence online shoppers. Text processing methods were used to preprocess customer evaluations of products. The Product review files are produced as a flat-file during pre-processing. After eliminating the stop words, the flat file is tokenized and the keywords are listed. Each word's frequency has been determined, and the subject with the greatest frequency count has been extracted. Similar comments are grouped together in each subject, and the resulting words are then categorised as either positive or negative. For ease of understanding, a chart is created from the categorised comments. Nave Bayes, Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor are just a few of the classification models that have been used to classify user evaluations. Models are evaluated by utilising 10-Fold Cross Validation (FCV).
The reach on the applications of internet is growing faster than any other technology. Mobile phones are becoming powerful and accessible to everyone. With the wider reach of Internet and smart phones, information explosion is happening in the field of information technology. It is very important to make required information available instantly for smart phone users. Hence, in this work, few enhancements are made by introducing cache sharing and pre-fetching together with noise reduction to improve overall performance of the mobile phone for optimal cache memory utilisation, network traffic reduction, efficient bandwidth utilization and latency reduction. Caching combined with prefetching is used to get frequent and recently used contents in cache of mobile phone without much delay. Whenever a new content is searched it is invariably fetched from the server. If users keep searching for new information more than reusing the content, then cache utilisation will be less and network traffic will be more resulting in more server hits and latency. In order to reduce server hits, sharing of cache contents amongst mobile users is effective. In the proposed method, the junk/noise contents are identified and removed from cache/pre-fetch area, to improve the overall performance. The experimental results revealed that the hybrid method with noise reduction has improved the cache performance much better than any other methods in the mobile phone environment.
Yoga has got a plethora of health benefits which improves flexibility, perfects body posture, builds muscle strength, and increases focus. A model is designed to help the yoga practitioners to save money on trainers and be self-paced to practice in any time as per convenience. For any yoga pose, three levels of classification are considered. They are body position, variation in the body position, and the actual yoga posture. The model will be built using a building block based on a variation of ResNet. Yoga-82, is a hierarchically labelled dataset used to train the models. The developed model will be able to help a beginner to learn various levels of classification associated with a particular yoga pose. A developed model is presented, showing significant performance improvement over the past models built for tracking the fitness of the people and gave a significant boost to the yoga applications.
Artificial Intelligence technology is being implemented in various industries, including healthcare, automotive, manufacturing, finance, and agriculture. It also supports these industries in overcoming traditional difficulties in increasing productivity and efficiency. Agricultural automation is a major source of concern and a hot topic around the world. The world's population is rapidly growing, and it comes with increased demand for food and work. The farmers' traditional practices are not sufficient to meet these objectives. In this paper it is examined how computer science is used in the farming and agricultural industries. An agricultural production system is being forced into a replacement paradigm, according to the Food and Agriculture Organization of the United Nations. Rapid population growth, shrinking farmland, depleting natural resources, erratic climate change, and shifting market demands are all contributing to this trend. The new agricultural system must be more productive in terms of production, more efficient in terms of operation, more resilient to global climate change, and more sustainable for future generations to be effective and sustainable. When it comes to processing data and generating patterns, deep learning is a synthetic intelligence function that mimics the human brain. Deep learning can be utilized in decision-making since it mimics how the brain works. Image processing and huge data analysis are among the advanced techniques that have tremendous potential in this field. Numerous applications of deep learning techniques are being explored in agriculture, including disease detection, fruit or plant classification, and agricultural management processes, among other things.
