Search

Abstract Background Artificial intelligence (AI) has shown great promise in the field of healthcare as a means of improving the diagnosis of skin cancer. The objective of this research is to enhance the precision and effectiveness of skin cancer identification by the incorporation of convolutional neural networks (CNNs) and discrete wavelet transformation (DWT). Making use of AI-driven techniques has the potential to completely transform the diagnosis process by providing quicker and more accurate evaluations of skin lesions. In an effort to improve dermatology and give physicians reliable resources for early and precise skin cancer diagnosis, this work explores the combination of CNNs with DWT. Methods The accurate and timely classification of skin cancer lesions plays a crucial role in early diagnosis and effective treatment. In this, we propose a novel approach for skin cancer classification using discrete wavelet transformation (DWT). The DWT is employed to extract relevant features from skin lesion images, which are then used to train a classification model. The effectiveness of the suggested approach is assessed through the examination of a dataset of skin lesion images with known classes (malignant or benign). Results The outcomes of the experiment demonstrate that the suggested model successfully attained a classification result of sensitivity as 94% and specificity as 91% when compared with artificial neural network (ANN) and multilayer perceptron methods. Conclusions The HAM 10000 dataset is employed to explore and evaluate the effectiveness of the proposed model, leading to improved accuracy compared to the existing machine learning algorithms in utilization. The results demonstrate the effectiveness of the DWT-based approach in accurately classifying skin cancer lesions, thus aiding in early detection and diagnosis.

In the present experimental effort is made to increase the performance of a solar still (SS) by including a novel design of a zig-zag-shaped air-cooled condenser (ZZACC) and cuprous oxide (CuO) as a nanomaterial. Research work is conducted in the climatic conditions of Gandhinagar, Gujarat, India, from September to November 2020. A comparison was made to assess the performance of a conventional solar still (CSS) and a solar still equipped with a zig-zag shape air-cooled condenser (SSWZZACC) with CuO. The experiments’ findings showed that adding CuO to SSWZZACC increases the distillate production by 46.83% and the daily energy efficiency by 45.98%, respectively, compared to CSS. Also, SSWZZACC demonstrates a better efficiency of exergy and latent heat of vaporization than CSS because CuO causes an increase in the evaporative heat transfer coefficient of water. In life cycle cost analysis study discovered that SSWZZACC has a 27.77% lower cost per litre of water (CPL) than CSS. The obtained maximum energy and exergy efficiency values for CSS and SSWZZACC were 2.36% & 25.75% and 3.9% & 37.59%, respectively. In economic and environmental aspects, it was found that SSWZZACC with CuO showed a cost-effective desalination unit and was highly effective from a carbon credit point of view (CCP) by CO2 mitigation.

Abstract The continuous rise in demand, combined with the depletion of the world's fossil fuel reserves, has forced the search for alternative fuels. The biodiesel produced from Roselle is one such indigenous biodiesel with tremendous promise, and its technical ability to operate with compression ignition engines is studied in this work. To characterize the fuel blends, researchers used experimental and empirical approaches while operating at engine loads of 25, 50, 75, and 100%, and with fuel injection timings of 19°, 21°, 23°, 25°, and 27° before top dead center. Results indicate that for 20% blend with the change of injection timing from 19° bTDC to 27° bTDC at full load, brake specific fuel consumption and exhaust gas temperature was increased by 15.84% and 4.60% respectively, while brake thermal efficiency decreases by 4.4%. Also, an 18.89% reduction in smoke, 5.26% increase in CO2, and 12.94% increase in NOx were observed. In addition, an empirical model for full range characterization was created. With an r-squared value of 0.9980 ± 0.0011, the artificial neural network model constructed to characterize all 10 variables was able to predict satisfactorily. Furthermore, substantial correlation among specific variables suggested that empirically reduced models were realistic.

A study has been conducted using a portable emission tester (PET) for the city of Imphal, India using twelve (12) key business locations for determining the combustion by-products (exhaust emissions) & their characteristics from both light and heavy-duty diesel vehicles. The ambient temperature varied from 23.5 to 28 °C and humidity to be 65 ± 15% during the course of the study. Higher values of CxHy and CO, compared to the standard permissible values, have been observed from light duty diesel (LDD) vehicles, with average values of 430 ppm and 465.4 ppm respectively, while NOx has an average value of 99.2 ppm. Similar characteristics are also witnessed from the heavy-duty diesel (HDD) vehicles with CO and CxHy having alarming values of 1585 ppm and 2968 ppm respectively, while NOx have an average value of 140.06 ppm. Using total emission approach, the CO2 emissions have been estimated to be about 38.02*105 tons and 1135*105 tons from LDD and HDD respectively till 2018. With respect to the present study, HDD have the maximum contribution of CxHy, NOx and CO in the atmosphere with values of 76.9%, 69.3% and 58.5% respectively. The authors believed that such alarming emissions from the vehicles would have been controlled if the authorities had performed regular checks and imposed strict laws against heavy emissions from vehicles.

Impact of different input variables on the thermal performance features of an automobile radiator was investigated, statistically analyzed, and optimized using the powerful technique-Taguchi's grey relational analysis (GRA) and Response surface methodology (RSM). Polyethylene glycol (PEG) nanofluids containing ZnO nanoparticles of various volume concentrations (0.2%–0.6%) were used. 1-Butyl 3-methylimidazolium bromide [C4mim][Br] ionic liquid was added to reduce particle accumulation and increase nanofluid dispersion. The mini radiator used was an unmixed crossflow type. The analyses were carried out at various flow rates. Thermal performance parameters like Nusselt number (Nu), heat transfer coefficient (htc), pressure drop, and pumping power were optimized by using weighted grey relational grade, depending on the experiments designed using Taguchi's Experiment Design. ANN modelling was used to get a better prediction of the non-linear form of critical data. Optimized Nu, htc, pressure drop, and pumping power were obtained for different combination of Re, air velocity, and nanofluid concentration to maximize. The htc estimated by ANN is found to be reasonably consistent with the experimental findings. From the research findings, it is also inferred that heat transfer enhancement does occur in radiators employing nanofluids but at the expense of the pressure drop and pumping power.

Researches in many laboratories on solar still desalination are concerned with increasing efficiency using only solar energy. One of the techniques is the difference in the thickness of the glass cover of the distiller. In order to see the influence of this parameter on efficiency, three similar stills with three different glass coverings were investigated. The flow of heat goes through the cover, and higher glass temperature leads to solar still productivity becoming lower. This paper presents an optimization of glass thickness (Gt) of a conventional solar still (CSS) in El Oued climate, Algeria. Based on the experimental results, the distilled water production rate, energy, and energy efficiency of the CSS have been discussed. The results showed that the suitable Gt of the CSS was 3 mm. The distilled water of around 3.15, 2.02, and 1.13 kg was produced by the CSS at energy efficiency of 30.71, 19.02, and 11.44% with the Gt of 3, 5, and 6 mm, respectively. The daily average exergy efficiency of 2.46, 1.38, and 0.84% was calculated for the CSS at Gt of 3, 5, and 6 mm, respectively.

This paper reported the experimental testing of a triangular and conventional basin solar still (TBSS and CBSS). Solar basin and absorber are made of glass and a polyethylene cover, respectively, with an area of 0.25 m2. Square and triangular absorber with the same area of 0.25 m2 with square and triangular glass cover for condensation was fixed. Experimentations were conducted during the month of December 2018, and different natural criteria such as intensity, wind speed, and surrounding ambient temperatures were considered. Also, the modified model was compared to the CBSS on its performance and efficiency characteristics. The experimental results also revealed that the temperature of the water inside the TBSS was higher as compared with that of the CBSS. The daily yield obtained from CBSS and TBSS was found to be 2.7 and 3.2 kg/m2, respectively. Also, the daily efficiency of the TBSS was improved by 11.36% than the CBSS.