Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment.

Enterprise games have an amazing ability to hold employee's attention, build loyalty, and increase motivation. The acceptance of game context in business applications is encouraging; also, it motivates employees to perform their tasks in an enjoyable manner. Enterprise gamification is growing at an impressive rate. This analysis focuses on how enterprises could use gamification for their benefits and discusses few implemented examples. The review concentrates on positive and negative aspects of gamification in business and discusses the mechanism that helps in implementing the games in an enterprise. Our findings revealed that, gamification has a fine line between being a positive tool to motivate employees and being a source of workplace pressure, which could affect the social and mental well-being.

With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The diagnosis of lung illnesses can benefit from the information a computed tomography (CT) scan can offer. The major goals of this study are to diagnose lung cancer and its seriousness and to identify malignant lung nodules from the provided input lung picture. This paper applies unique deep learning techniques to identify the exact location of the malignant lung nodules. Using a DenseNet model, mixed ground glass is analyzed in low-dose, low-resolution CT scan images of nodules (mGGNs) with a slice thickness of 5 mm in this study. This was done to categorize and identify many histological subtypes of lung cancer. Low-resolution CT scans are used to pathologically classify invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA). 105 low-resolution CT images with 5 mm thick slices from 105 patients at Lishui Central Hospital were selected. To detect and distinguish, IAC and MIA, extend and enhance deep learning two- and three-dimensional DenseNet models are used. The two-dimensional DenseNet model was shown to perform much better than the three-dimensional DenseNet model in terms of classification accuracy (76.67%), sensitivity (63.3%), specificity (100%), and area under the receiver operating characteristic curve (0.88). Finding the histological subtypes of persons with lung cancer should aid doctors in making a more precise diagnosis, even if the image quality is not outstanding.

The use of Campus Management Systems (CMSs) has increased dramatically in Pakistani universities, a huge amount of money has been invested in its development and deployment. The CMS provides an integrated platform for managing academic activities, controlling process flows and provide online access to related information. It improves the efficiency and effectiveness of the universities and eventually improves the quality of teaching. However, it is very important to consider the attitudes and perceptions of faculty members and students towards the adoption of CMS because they may affect the acceptance of this technology. The aim of this study is to investigate and highlight the user satisfaction level about the CMS quality according to ISO/IEC 9126 standard. This work uses cross-sectional design as a primary research method, and data from 105 students and faculty members were collected from a Pakistani public sector university using a questionnaire survey. The response of the respondents illustrates that the system functionality is good, reliable, usable, and efficient. However, some improvements are necessary in some areas such as understandability, learnability, operability, and attractiveness.