PVA Based Polymer Electrolyte with Layered Filler Graphite for Nat­ural Dye Sensitized Solar Cell

Graphite nanopowder is synthesized by mechanical method using ball mill and used as filler in polymer electrolyte film based on Polyvinyl alcohol(PVA)for application in natural dye sensitized solar cell(DSSC).In the present work dye sensitized solar cell has been assembled using electrolyte system composed of PVA as host polymer,ethylene carbonate as plasticizer,LiI:I2 as redox couple and graphite as filler;TiO2 modified with Copper oxide(CuO)photoanode in order to provide inherent energy barrier and natural cocktail dye as sensitizer.The obtained solar cell conversion efficiency was about 3.2%with fill factor 52%using an irradiation of 100 mW/cm^(2) at 25℃C.

Medical Internet of things using machine learning algorithms for lung cancer detection

This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT devices.In this work,a review of nearly 65 papers for predicting different diseases,using machine learning algorithms,has been done.The analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical IoT.Each technique was analyzed on each step,and the overall drawbacks are pointed out.In addition,it also analyzes the type of data used for predicting the concerned disease,whether it is the benchmark or manually collected data.Finally,research directions have been identified and depicted based on the various existing methodologies.This will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws.

A novel strategy for automatic test data generation using soft computing technique

Software testing is one of the most crucial and analytical aspect to assure that developed software meets pre- scribed quality standards. Software development process in- vests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear struc- ture of software. Moreover, test case type and scope deter- mines the quality of test data. To address this issue, software testing tools should employ intelligence based soft comput- ing techniques like particle swarm optimization （PSO） and genetic algorithm （GA） to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing ex- periments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test ad- equacy criterion as branch coverage. The performance ade- quacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work.

An interactive web-based solar energy prediction system using machine learning techniques

Solar energy being one of the most inexpensive renewable energy sources,has shown to be a viable alternative to traditional fossil-fuel and wood-based electricity generation.For the purpose of creating a more trustworthy and successful energy planning strategy,accurate projections of sun irradiation,solar energy generation,and revenues are crucial.Hence,in this work we have proposed webbased optimal prediction system that estimates solar radiation based on location and meteorological data using Machine Learning techniques.Furthermore,an interactive dashboard solar digital map has been developed that enables real-time investigation of solar energy consumption,production,solar radiation,and investment potential for a specific county in California.The model’s performance has been measured using Root Mean Square Error(RMSE),Mean Square Error(MSE),Mean Average Error(MAE),and Mean Absolute Percentage Error(MAPE)scores.Experimental results demonstrate that stacking model outperformed all the models with the lowest RMSE,MSE,and MAE.