The phases of drug discovery and development, starting with drug design and moving through crucial clinical trials to clinical practice, are being revolutionized by machine learning techniques. A more sophisticated kind of learning is machine learning (ML), which may be used to identify patterns in the solutions to solve current problems and learn to solve unsolved problems in future. Following the pre-processing of the necessary data, the objective is to learn from the data and identify patterns. The results can then be obtained by applying the patterns found to a different dataset. In general, when using larger, more agile data samples on high-dimensional datasets, ML algorithms can make predictions that are more accurate. Along with the rapid development of bioinformatics, there has been a significant global evolution in drug discovery and design technologies over the past ten years. Various artificial intelligence and machine learning (ML) methodologies have been used in drug discovery and design, leading to changes in all of their stages, including the time-consuming drug-target interaction (DTI) prediction and medicines and compounds discrimination.

The way energy is used in the modern world is changing. Solar energy is outpacing other energy sources in this race for exploration for a variety of reasons. As a result, both the energy and the parameters of a solar Photovoltaic (PV) system must be constantly monitored. This study looks into several Internet of Things (IoT) based electronic modules for data processing that are used to monitor solar PV system parameters (electrical and environmental). Each technology is thoroughly examined, including its introduction, design features, communication, and limitations. Furthermore, the article analyzed several common elements of each IoT-based module, such as data processing, size, and cost, and made recommendations based on the results.

Technologies involved CMOS, are reaching the nano processing realm by resulting in several scaling challenges in the case of complementary MoSFETs, which have a short level of channel in their effect. The process of variation influences the design performance and different parameters of the device. FinFET makes a new outstanding effort, which incorporates better level of control in the conditional channel. Also, its lower performance makes a change in 6T Static process Random Access Memory through circuit function design. It reduces the bit line with loading effect, which in turn improves Static Random-Access Memory and their performance level. 6T SRAM with conventional level of cell suffers very serious conditional stability level of degradation problems. It processes their disturbance in the low-level power type mode. 6T SRAM faces major problem in the output level voltage with a highly reduced level of threshold type voltage conditional transistor, which will destroy whole of the read operation in the 6T type SRAM cell. Noises make it very easy to destroy the stored level data to nodes with 6T Static Random Access Memory cell. It makes the direct as the path in between storage nodes and their bit lines. This paper needs to overcome the 8T level SRAM as cell in their proposed read where the whole stability is expected to improve. Level of this makes in simulation of evaluating the performance level of the FinFET-level based 6T conditional SRAM, 8T level SRAM and 10T type SRAM as cells and need to compare the results by micro wind tool.

Every day, farms produce thousands of information points on temperature, soil, usage of water, atmospheric phenomenon, etc. With the assistance of computer science and machine learning models, this data is leveraged in real-time for obtaining useful insights like choosing the correct time to plant seeds, determining the crop choices, hybrid seed choices etc. Keywords: Artificial intelligence, agriculture robots, agriculture, intelligent spraying, temperature, soil, water, machine learning Cite this Article G. Ramachandran, T. Sheela, S. Kannan, A. Malarvizhi, G. Sureshkumar, P.M. Murali, G. Murali. Applications of Artificial Intelligence in Agriculture. Journal of Computer Technology & Applications. 2020; 11(1): 1–3p.

Artificial intelligence (AI) is a branch of computer science capable of analyzing complex medical data. This can be used in diagnosis, treatment and predicting outcome in many clinical scenarios. Medicine and internet searches were carried out using the keywords ‘artificial intelligence’ and ‘neural networks’. An overview of different AI techniques is presented in this paper along with the review of importance in clinical applications. The proficiency of AI techniques has been explored in almost every field of medicine. This paper mainly deals with tumors. An artificial neural network was the most commonly used analytical tool. Other AI techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent system have all been used in different clinical settings. Keywords: Automation, artificial intelligence (AI), robotics, neural networks, machine learning Cite this Article Ramachandran G, Sheela T, Sureshkumar G, et al. Artificial Intelligence in Medical Fields. Research & Reviews: Journal of Medical Science and Technology . 2019; 8(1): 1–3p.

Medical imaging may be a heatedly contested field wherever winning merchandise maximize healthcare price by providing the simplest pictures within the shortest quantity of your time to assure correct designation and treatment for patients whereas maximizing the potency of workers and facilities. The advancement of medical imaging has resulted in terribly giant knowledge sets and progressively advanced algorithms golf stroke ever-growing demands on process power and gap plenty of opportunities for performance improvement. Keywords: Computed axial tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), medical, scanners Cite this Article G. Ramachandran, T. Muthumanickam, T. Sheela, A. Malarvizhi, G. Sureshkumar, S. Kannan, G. Murali, R. Sankarganesh. Medical Healthcare Applications. Research & Reviews: Journal of Medicine . 2019; 9(3): 35–38p.

Internet of Things (IoT)-enabled devices have made remote monitoring in the healthcare sector possible, unleashing the potential to keep patients safe and healthy, and empowering physicians to deliver superlative care. It has also increased patient engagement and satisfaction as interactions with doctors have become easier and more efficient. Furthermore, remote monitoring of patient’s health helps in reducing the length of hospital stay and prevents re-admissions. IoT also has a major impact on reducing healthcare costs significantly and improving treatment outcomes. Internet of Things, patients’ interactions with doctors were limited to visits, and tele and text communications. There was no way doctors or hospitals could monitor patients’ health continuously and make recommendations accordingly. Keywords: Hospitals, patients, internet of things Cite this Article G. Ramachandran, S. Kannan, T. Sheela, et al. Internet of Things in Healthcare. Research & Reviews: Journal of Medical Science and Technology . 2019; 8(1): 10–12p.