Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

Cloud robotics is a field of robotics that endeavors to summon cloud advances, for instance, cloud computing, and other internet advances focused on the advantages of merged framework and shared administrations for robotics. In this research, an intelligent task planning mechanism is proposed to attain a certain level of attention for knowledge sharing among cloud-based robots using mamdani fuzzy inference system (MFIS). The proposed task planning mechanism using mamdani fuzzy inference system (TPM-MFIS), can classify the different stages of attention such as very high, high, medium, low, very low and none. In this recommended intelligent system, cloud robotics services are used to maintain the workload of each robot. Whereas, all fuzzification and decision-making policies handled on cloud to avoid the redundancy task planning consumption on individual level. This system has two input variables that are facial expression and motion rate. Input variables detect the output level of attention to be very high, high, medium, low, very low, and none. This paper presents an analysis of the result’s accuracy using proposed TPM-MFIS to model the task planning process.

Within the past few years, organizational security has emerged as a critical challenge for industries, security agencies, academic institutions and organizations. The current security system lacks efficiency which influences organizations' security parameters negatively. While dealing with a violent or terroristic activity human guards may not be able to response effectively under pressure, due to some limitation or lack of continuous attention. In present conditions where guards are not active enough to generate a quick response against threat, human eagerly need to design an effective virtual security system which would be responsible to analyze collective measures, process, and procedure that ensure protections of virtualization environment. In order to increase efficiency of security, eliminate security holes and to obtain quick response towards suspicious activities, there is need to replace humansecurity-guard with a virtual-security-guard system. This virtual guard will be an embedded machine, which will act like a complete security guard that will work under 'Human Based Psychology'. This system would be reflection of an ideal virtual security guard which will generate intelligent and quick response towards abnormal activities by making intelligent decisions based on selective attention mechanism. The proposed system can be efficient enough to focus both functional and non-functional requirements (resistibility in smoke, fog, rain, dark and dust).