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The human skin is an extraordinary structure that frequently encounters both known and unknown diseases. Conducting regular and thorough skin examinations plays a vital role in the early detection of any detrimental or incipient changes in the skin, which could potentially lead to skin disease. This research paper is dedicated to addressing this challenge by proposing an effective solution using ML models, specifically a Convolutional Neural Network (CNN) system. The proposed CNN model exhibits superior accuracy in recognizing different types of skin diseases. By harnessing the power of machine learning techniques, it contributes to the development of robust systems capable of detecting various classes of skin ailments. In this paper, the CNN machine learning algorithm is chosen and implemented on a dataset of skin infection cases to accurately predict the specific class of skin disease. Among the numerous machine learning algorithms explored, CNN stands out as the optimal choice for effectively detecting skin diseases, providing precise disease identification, and reporting the corresponding accuracy levels., {"references":["1.\tAl Abbadi, N. K. (2010). Psoriasis detection using skin color and texture features. Journal of Computer Science, 6(6), 648-652.","2.\tArifin, M. S., Kibria, M. G., Firoze, A., Amini, M. A., & Yan, H. (2012, July). Dermatological disease diagnosis using color-skin images. In 2012 international conference on machine learning and cybernetics (Vol. 5, pp. 1675-1680). IEEE.","3.\tALEnezi, N. S. A. (2019). A method of skin disease detection using image processing and machine learning. Procedia Computer Science, 163, 85-92.","4.\tKumar, V. B., Kumar, S. S., & Saboo, V. (2016, September). Dermatological disease detection using image processing and machine learning. In 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) (pp. 1-6). IEEE.","5.\tAjith, A., Goel, V., Vazirani, P., & Roja, M. M. (2017, June). Digital dermatology: Skin disease detection model using image processing. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 168-173). IEEE.","6.\tPołap, D., Winnicka, A., Serwata, K., Kęsik, K., & Woźniak, M. (2018). An intelligent system for monitoring skin diseases. Sensors, 18(8), 2552.","7.\tPołap, D., Winnicka, A., Serwata, K., Kęsik, K., & Woźniak, M. (2018). An intelligent system for monitoring skin diseases. Sensors, 18(8), 2552.","8.\tDhillon, A., & Verma, G. K. (2020). Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), 85-112.","9.\tLK, S. S., Ahmed, S. T., Anitha, K., & Pushpa, M. K. (2021, November). COVID-19 outbreak based coronary heart diseases (CHD) prediction using SVM and risk factor validation. In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-5). IEEE.","10.\tSowmya, S. L. K., Guptha, N.S., Shruthi G., Anitha, T. K. (2019). Detection of Liver Lesion using ROBUST Machine Learning Technique. International Journal of Engineering and Advanced Technology (IJEAT), 8(5)."]}

Polymer systems like epoxies are not generally used in the manufacturing of structures on their own since they possess an ordinary level of mechanical properties. Hence, different reinforcing fibre...

Dehumidifier is that the most vital unit in liquid drier air conditioning system, this paper experimental analysis of solar assisted liquid desiccant cooling system (SALDCS) by Li Cl-H2O as a desiccant. A capable solar energy and cooling technique is through the employment of a liquid drying agent system, wherever humidness is absorbed directly from the method air by direct contact with the drying agent, the desiccant is then regenerated by solar hot water or air. The solar assisted desiccant dehumidifier system is operated variable operating conditions, air flow rate (0.05 to 0.11 kg/sec), desiccant concentration (30% to 40%) and desiccant temperature (maximum 40oC). Effectiveness of dehumidifier verified by experimentally, it results effectiveness decreased by the air flow rate & desiccant temperature, increased by desiccant concentration, the same process done with inter cooler (Indirect evaporative cooler) better dry-air cooling rate observed.

Fracture toughness ( K IC ) and fatigue crack growth rate (FCGR) are the important material properties in fracture mechanics. ASTM- E399 and ASTM- E647 are the standards for determination of K IC and FCGR of metallic materials. These standards recommend the use of compact tension (CT) or single edge notched bend (SENB) test specimens. Literature review indicates that CT or SENB specimens are complex in nature, difficult to manufacture, require typical fixtures for loading during experimentation and the test procedures using CT or SENB are time consuming and cumbersome. An alternate specimen geometry which can overcome the above said drawbacks is needed by the industry which can be used as standard test specimen geometry. This research work explains use of circumferentially cracked round bar (CCRB) specimens of high strength Al2014T651 alloy for determination of K IC and FCGR. The pre-cracked round bar specimen was loaded in tensile in a universal testing machine and pulled till failure. Using suitable stress intensity factor equations the fracture toughness can be calculated. In case of crack growth test, the pre-cracked round bar specimen is allowed to rotate under fatigue load. The ratio of length of crack propagated to the number of cycles to failure was the crack growth rate. The SEM analysis of fractured surfaces was also done. The results are comparable with the values reported in the literature obtained by using standard test specimens. There are numerous advantages of using round bar specimen in K IC and FCGR tests. It is concluded that, the methodology of determination of fracture toughness and fatigue crack growth rate using CCRB specimens is relatively simple, reliable, fast and economical. CCRB specimen may be recommended as a standard test specimen for fracture toughness as well as crack growth tests.