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The paper describes about the dynamic electric vehicle charging system from solar. It reduces fuel and pollution. Nowadays electric vehicle are growing in numbers because of high rates of fuel Electric vehicle has now hit the road worldwide and are slowly growing in numbers. Also it is proven that electric vehicle helpful in reducing cost of travel and which is cheaper than fuel. So we have developed an electric charging system with unique ideas for solving charging system problem. In this electric vehicle no need of any wire, no need of external power supply, no need to stop vehicle for charging, we can charge EV in moving condition. In this system we use, battery, transformer, Atmega controller, LCD display, regulatory circuit, solar panel, coils of copper, AC to DC converters to develop the system more accurate. This whole system describes that how an electric vehicle can be charged in moving condition and also eliminating the need to stop the vehicle and charge it. Thus, insulating in the copper coil in the road, we can charge the electric vehicle in the moving condition.

Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.

INTRODUCTION: The menace of oral cancer has become a public health concern and its prevalence is increasing with every passing day, making it the sixth most common cancer across the globe. OBJECTIVE: To evaluate the prevalence and risk factors for potential oral malignant disorders in a middle aged north Indian population. MATERIALS AND METHODS: This Cross-sectional study was carried out on 1260 individuals in Delhi and NCR. A questionnaire was created to record information about socio demographic traits, oral hygiene routines, threat associated to oral probable malignant disorders .The Oral Mucosal lesions were examined by professionals. Results. The Overall Prevalence of Potential oral malignant disorders was found to be with oral submucous fibrosis (6.09%) found in a greater extent and erythroplakia (0.15%)found to be lesser. RESULTS: It was documented that 11.48% of the Northern Indian Population which suffered from oral malignant lesions, with the prevalence rates of leukoplakia in 3.01%, Lichen Planus in 1.42% and lowest being erythroplakia in 0.15% of the subjects. The prevalence of oral malignant lesions among males was found to be higher(12.8%) as compared to females p value

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

Price-based Demand Response (PBDR) programs have been envisaged to motivate customers for changing their consumption patterns by reducing peak demand against financial gains. DR is an emerging strategy to deal with peak demand, energy management, line congestion, reliability, security and economy of contemporary distribution systems. Therefore, it becomes essential to assess overall impact of DR prior to its implementation. However, the job is quite tedious as demand of the customers also depends upon socio-economic factors, human psychology, ambient conditions, etc., besides the dynamic price signal. This paper proposes a comprehensive, more realistic, simple and adaptive mean price-based DR modelling to assess overall impact of PBDR while addressing stochastic demand, and price elasticity of demand (PED) for individual customer. The model suggests dynamic PED to capture the stochastic DR of customers against real-time price signal using historical data. In addition, several DR indices are suggested to assess the impact of DR on several stakeholders. The study on a standard test bench reveals that proposed methodology is simple, robust, quick, and can deliver tangible information in the interest of concerned stakeholders.

The power system is growing from conventional grids to the smarter grids. The development of smart grids has facilitated the communication infrastructure between the grid and the end-user, which enables the grid to communicate with the customer and vice versa. This development motivated the researchers to gain interest in the area of Demand Response (DR). DR is capable to alleviate the economy of the distribution systems, however, its impact percolates to centralized generating stations via transmission systems. An extensive research work has been carried on DR pertaining to vital concerns such as economy, efficiency and reliability of the distribution systems. A brief review of this research is duly addressed by giving special attention to several methodologies, formulations and optimization techniques suggested and also throw some light on the issues and concerns of DR which are yet not being explored, but may provide new dimensions to operate future distribution systems.

The transformation of conventional grids into smart grids evolves bidirectional communication between the customer and utility. This development intends utility to monitor and modify the energy consumption patterns of the customers by floating various Demand Response Programs (DRPs). DRPs are known to be the most effective solution to the various techno-economic problems of the distribution systems. Moreover, its impact percolates through the entire power system. This paper presents an in-depth overview about definition, importance, implementation and outcomes of DR in modern distribution systems. The paper critically analyses and presents several vital issues, concerns and implications of various DRPs being implemented in practice. The paper also highlights some realistic directives to overcome certain limitations of existing DRPs on account of technical, social, economic and geographical concerns.

The objective of this research is to devise a cognitive behavioral therapy system or a Therapy Chatbot capable of catering to the health and informative needs of a user. Also, the key focus is on the therapy element which detects the level of depression in a particular individual. With the increasing stress in day to day life, every individual is prone to depression and the consequences of depression are disastrous, therefore this study revolves around helping people against depression using technology. Since depression has been the major cause of suicide among people, hence, an attempt to lessen the chances of depression and thereby suicide has been made. This study is intended to target 300 million people worldwide. The system not only detects the level of depression in an individual but also suggests remedies to lower the level of depression in an individual. Apart from this, the system caters to the informative needs of the user by helping him make a search for name, place, things, check weather conditions etc. This study uses python as the base language which can be integrated with android so as to serve as a messaging platform so that it can target a larger audience. It is designed in such a way that it can emulate a psychotherapist.

Many real world business processes are executed without explicit orchestration and hence do not generate structured execution logs. This is particularly true for the class of business processes which are executed in service delivery centers in emerging markets where rapid changes in processes and in the people executing the processes are common. In such environments, the process execution logs are usually natural language descriptions of actions performed and hence are noisy. Despite the lack of structured logs, it is crucial to know the trace of activities as they happen on the ground. Without such a visibility into the ground activities, regulatory compliance audit, process optimization, and best practices standardization are severely disabled. Process monitoring on top of unstructured execution logs has been a relatively unexplored research area. This paper proposes an approach for process trace identification from unstructured logs that applies state-of-the-art text mining techniques. It applies this approach on logs of a real-world business process used in a service delivery center and shows that individual process activities are correctly identified 90% of the time. Also, 65% of the activity traces were identified with zero errors and an additional 24% with a single error. This approach is generic and applicable to a wide array of business processes.

Introduction: Ground water is the ultimate and most suitable fresh water resource for human consumption in the urban areas of India. Studies regarding ground water quality have shown that the higher rate of exploration as compared to the rate of recharging, inappropriate dumping of solid, as well as liquid waste, lack of strict enforcement of law has led to the deterioration of ground water quality. The present study was thus, carried out to evaluate physicochemical, as well as a microbiological profile of tap water, and filtered water in urban areas of Patiala, Punjab. Materials and Methods: The three zones under Municipal Corporation and two areas under Public Health Department were chosen according to the simple random sampling from Patiala city. From each area, 10 houses were chosen according to the systematic random sampling technique (n = 50). Water was taken from two sources, tap water, and from the water filter. Two samples were taken from each source one for the physicochemical analysis and another for bacteriological analysis. The samples which were sent for bacteriological assessment were collected in a sterile container. Results: The number of water samples found to be within desirable limits with respect to physicochemical parameters were significantly more with the filter water sample than the tap water samples. Suspicious/unsatisfactory microbiological quality of water was observed in 28% and 4% of tap and filter water samples, respectively. Conclusion: The results indicate that certain chemical parameters such as hardness, chloride, and fluoride levels were beyond the permissible limits. Therefore, we recommend that home filters should be installed, serviced appropriately, and their water quality should be checked routinely. Also, any leak from sewage pipes should be promptly repaired to prevent contamination of drinking water.