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As demonstrated in hereditary disorders and acute coronary syndrome, facial and physical signals (clinical gestalt) in Deep learning (DL) models enhance the evaluation of patients' health state. It is unknown whether adding clinical gestalt enhances the classification of patients with acute illnesses. The applicability of clinical gestalt may be assessed using simulated or augmented data, similar to earlier work on DL analysis of medical images.. In this study, using photos of facial cues for disease, For automatic rug sick identification, we developed a computer-aided diagnosis method. Individuals who were experiencing an acute sickness were seen by uninformed observers to have pale skin, lips and a more bloated face, more droopy eyelids, redder eyes, less shiny and spotted skin, as well as seeming more weary.. According to our research, critically ill and potentially contagious individuals can be identified using facial clues related to the skin, lips, and eyes. 1 To address the lack of data, we used deep transfer learning and constructed a CNN framework using the four transfers learning techniques shown below.: ResNet50, InceptionV3, VGG16, VGG19, Xception, and Inception. Whereas ResNet101 is utilized in the current methods, it does not have the appropriate precision and could use improvement. So, it is suggested to combine the current method with additional transfer learning techniques. The suggested method was examined using a publicly accessible dataset called Facial Cue of Illness.
As demonstrated in hereditary disorders and acute coronary syndrome, facial and physical signals (clinical gestalt) in Deep learning (DL) models enhance the evaluation of patients' health state. It is unknown whether adding clinical gestalt enhances the classification of patients with acute illnesses. The applicability of clinical gestalt may be assessed using simulated or augmented data, similar to earlier work on DL analysis of medical images.. In this study, using photos of facial cues for disease, For automatic rug sick identification, we developed a computer-aided diagnosis method. Individuals who were experiencing an acute sickness were seen by uninformed observers to have pale skin, lips and a more bloated face, more droopy eyelids, redder eyes, less shiny and spotted skin, as well as seeming more weary.. According to our research, critically ill and potentially contagious individuals can be identified using facial clues related to the skin, lips, and eyes. 1 To address the lack of data, we used deep transfer learning and constructed a CNN framework using the four transfers learning techniques shown below.: ResNet50, InceptionV3, VGG16, VGG19, Xception, and Inception. Whereas ResNet101 is utilized in the current methods, it does not have the appropriate precision and could use improvement. So, it is suggested to combine the current method with additional transfer learning techniques. The suggested method was examined using a publicly accessible dataset called Facial Cue of Illness.
The fine coefficient is one of the physical properties of the fine aggregate. The fineness modulus (FM) is obtained by adding the total percentage of sand retained on each sieve and dividing by 100. The fineness factor varies depending on the resources available. Sand is widely used in construction. Due to excessive use, the supply of sand is decreasing day by day, is it necessary to find an alternative material for sand in the future? And on the other hand, the amount of construction and demolition waste is increasing every day and is still underutilized, and this is the main focus of research on the use of C and D waste mortar material in conventional construction. To analyse the waste mortar material, a material sample is collected from the site and the physical properties of the sand are checked and compared with the standard zones of sand according to the compressive strength IS using C and D waste mortar mixture in a ratio of 1:3 Casting. The 7.06 cm cube size is compared to 3-day and 7-day strength.
When the employees don’t consistently attend their jobs in their workplace at a scheduled time or if their absence is deliberate or unaccounted for with no reliable reason given then its treated as absenteeism. Failure of the employee to attend work during on duty hours can also be termed as absenteeism as per theIndian Factories Act 0f 1948. To understand this problem better a study was undertaken in five garment factories. Sample size taken was one hundred and fifty and simple random sampling was followed.Research gap showed that continued absence might be due to increased work stress, work burnout, exhaustion and disinterest in work. It may also be due to lack of work satisfaction which was rated as one of the main reason for continued absence from the work spot.The research methodology consisted of both primary as well as secondary data .The data analysis was based on the primary data and description method was followed. The objectives consisted of finding out the reasons for absenteeism and whether it was due to the strenuous modern work culture? Suggestions were made regarding the employees who applied for long leave and never turned up on their work spot for along period which was prevalent among the floating population working in garments in Bangalore. Many workers were mentally and physically exhausted and flexi timings and shift system, sick leave facility would ease their tiredness and monotony at work. The management of these organizations have taken many employee friendly measures to ease the work atmosphere and environment such as increasing the wages of the employees from time to time and even paying extra for overtime. The importance of harmonious industrial relations cant be stressed. With both government as well the organizational management not in favour of trade unions post industrial policy of 1991 the workers themselves have to align with their company’s policies and look for ideal ways within organizational framework to solve their work issues.
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