Volume 7 | Issue 1 |

 Abstract

Nowadays, the primary computer device is a mobile phone. People are often requesting more resources than a mobile device can provide. To counteract this, a mobile device needs get assistance from an outside source. One of such channels is cloud computing. The emergence of mobile cloud computing and its potential development are discussed in this essay on cloud computing. It compares the ideas of mobile cloud computing and cloud computing, as well as their features, benefits, implementations, implementation issues, potential solutions, future scope, and analysis.

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 Keywords

Mobile Cloud Computing; Mobile Computing; Cloud Computing; Research Directions

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 Abstract

In this paper we present some approaches of methods in multi-objective linear programming in different field of Integer programming the basis concept of these available exact methods.

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Multi-Objective Integer Linear Programming, Optimization Techniques

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Ayurveda, which means the science of life and is a medicinal system in India, derives from the words "aye," which means life, and "veda," which means to know. In 1930, Mr. Manal founds the Himalaya Pharmaceutical Company. A few years later, his older brother Mr. Misal joined him, and the two of them began expanding the business. An trade or transaction meant to satiate human needs or wants is at the heart of marketing. In other words, marketing is an activity carried out by people to fulfil their needs and desires through an exchange process. A demand is a wish that the customer is willing to spend money for. Anything or a service that a consumer wants or seeks is considered a want. When supported by the ability to purchase, desires become demands. A needs is anything the consumer feels to keep himself alive and healthy.Thisrange offers the goodnessof naturalsolutions for daily use,withnosideeffects. Personal care products is one of the very essential and common use consumer products foreveryindividualintheirdayto daylifeirrespectiveofrichandpoor, casteandreligion, etc.,inorder to facilitatethepresentstudyfirstofallitwillbebenefitforustohaveabirdseyesinviewofHimalayaherbalmarket in Coimbatorecity.

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A REVIEW ON CUSTOMER SATISFACTION WITH HIMALAYA PRODUCTS, PREFERRING PARTICULAR CITIES

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Dysgraphia is a condition that affects writers' ability. Identification of dysgraphia at an early stage of a child's development is a difficult task. It may very easily be identified using challenging skills associated to the Dysgraphia issue. Space knowledge, copying skills, and Visual Spatial Response are some of the elements used in this review engine capacity for Dysgraphia identifiable evidence. Using an element selection technique, the factors influencing dysgraphia inability is examined EN (Elastic Net). AI tactics are used to organize the crucial provisions. On the Dysgraphia dataset, the models considered in this sequence are KNN (K-Nearest Neighbors), Naive Bayes, Decision tree, Random Forest, and SVM (Support Vector Machine). Results demonstrate the best of the Random woodland arrangement model for Dysgraphia recognizable proof.

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Dysgraphia, Learning hardships, Machine Learning.

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Credit card theft has increased as e-commerce has expanded. Banks are having a harder time identifying credit card fraud as a result of the industry's explosive growth. One of the most crucial tasks that machine learning performs in preventing credit card fraud is the identification of fraudulent purchases. Banks estimate these types of transactions using a range of machine learning techniques, building on historical data and adding special characteristics to boost prediction accuracy. The strategy for sampling the data set, the choice of variables, and the detection process are all crucial to the effectiveness of credit card fraud detection. The usefulness of decision trees, random forests, and logistic regression in identifying credit card fraud is examined in this study. A dataset of 2, 84,808 credit card transactions from a European bank collected using kaggle. It defines fraudulent transactions as "positive class" and valid purchases as "negative class," but the data set is considerably skewed, with just about 0.172% being fraudulent and the rest being legitimate because to the author's frame interpolation efforts, the dataset now contains 60% fraudulent and 40% valid transactions. The dataset is subjected to each of the three approaches, and the output code is written in the programming R. Several metrics, including as sensitivity, specificity, accuracy, and error rate, are utilized to analyze the efficacy of the approaches in relation to the aforementioned criteria. The accuracy of the logistic regression, decision tree, and random forest classifiers was 90.0, 94.3, and 95.5%, respectively. When evaluated to logistic regression and decision trees, the Random forest outperforms both.

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EFFECTIVE DETECTION OF CREDIT CARD FRAUD USING LOGISTIC REGRESSION, DECISION TREE AND MACHINE LEARNING TECHNIQUES

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Wireless Sensor Networks are usually composed of a lot of distributed sensor nodes that organize themselves into a multi-hop wireless network. Several of the main problems in wireless sensor networks are actually energy usage, lack of authentication data integrity as well as instability of load link between sensor nodes which brings down the acceptance of the sensor network. The latest communications in wireless sensor networks (WSNs) have a lot of new energy-efficient protocols especially designed, in which energy recognition is actually an important concern. Inside WSNs, many small sensor nodes are utilized as a good means of data collecting in a variety of environments. Since the sensor nodes run on the battery of power that is restricted, it's an excellent demanding goal to develop an energy-efficient routing protocol, which may lessen the delay while providing long span and high energy efficiency of network lifetime. Our proposed item is actually improved from LEACH protocol by adapting node scheduling program (active and sleep) in heterogeneous and homogeneous node topologies of WSNs, which yield better performance in energy saving and then improved network lifetime. The work carried out is presented in this thesis will be based on simulations of newly designed routing protocols, from the key routing protocol of clustering based hierarchical, routing protocol namely called LEACH (Low Energy Adaptive Clustering Hierarchy). Simulations allow for more aspects to be evaluated and more parameter values to be investigated, than what would be possible using real applications in real wireless sensor networks. Nevertheless, the accuracy of every simulation is dependent on the accuracy of the simulation models used in the developed routing protocols

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Leach Algorithm, Energy Efficient Routing, Wireless Sensor Networks, energy efficiency etc.

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 Abstract

Sarv Skihsa Abhiyan

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Sarv Skihsa Abhiyan

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In past few years, the world has experienced a high rise in the market of poultry industry. This industry has added a non degradable feather waste to the environment on very large scale. This feather is even not degraded by the enzymes such as Trypsin and Pepsin and has become a global issue. Use of microorganisms in feather degradation is a trending and eco-friendly solution. The ultimate aim of the current study was to degrade feather using keratinolytic bacteria. Bacteria were isolated and screened from the soil samples collected from the dumping yards of chicken shops, poultry farm and feather waste collected areas. Total 68 different bacterial isolates were isolated and checked for their potential to degrade feather waste. Among them isolate C1B is gram positive, rod shaped, endospore forming bacterial species , morphological and biochemical identification showed that isolate C1B was Bacillus species. It was found that the bacterium had the efficiency of degrading 84.5% of feather within 72h. The bacteria used feather keratin as a principal source of carbon, nitrogen, sulphur and energy. The present study would highlight the degradation of feather waste using bacteria.

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 Keywords

Poultry Industry, Feather Waste, Waste Degradation, Bacteria, Keratin.

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Machine learning, Heart Disease, Decision Tree, Naive Bayes, Neural Network, Deep Learning and SVM.

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This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable.This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

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 Keywords

DC Microgrid, DG, Renewable Energy, Power Electronics, Energy Storage, Control Strategies, Integration Challenges.

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