In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health... Show More

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.

Melanoma is a harmful disease among all types of skin cancer. Genetic factors and the exposure of UV rays causes melanoma skin lesions. Early diagnosis is important to identify malignant melanomas... Show More

Melanoma is a harmful disease among all types of skin cancer. Genetic factors and the exposure of UV rays causes melanoma skin lesions. Early diagnosis is important to identify malignant melanomas to improve the patient prognosis. A biopsy is a traditional method which is painful and invasive when used for skin cancer detection. This method requires laboratory testing which is not very efficient and time-consuming to detect skin lesions. To solve the above issue, a computer aided diagnosis (CAD) for skin lesion detection is needed. In this article, we have developed a mobile application with the capabilities to segment skin lesions in dermoscopy images using a triangulation method and categorize them into malignant or bengin lesions through a supervised method which is convolution neural network (CNN). This mobile application will make the skin cancer detection non-invasive which does not require any laboratory testing, making the detection less time consuming and inexpensive with a detection accuracy of 81%.

Diabetes is a chronic disease that can affect the life of people due to high sugar level in their blood. The sugar level is increased due to a lack of production of insulin in the human body. Large... Show More

Diabetes is a chronic disease that can affect the life of people due to high sugar level in their blood. The sugar level is increased due to a lack of production of insulin in the human body. Large numbers of people are affected with diabetes and it can increase tremendously due life style behavior. Diabetes can also affect the other human organs, like kidneys, hearts, retinas and lead to the failure of these organs. This article presents a diabetic monitoring system to determine the risk of diabetes based on the personal health record of patients. In this work, several rules are designed based on the clinical as well as non-clinical symptoms. The effectiveness of the diabetes monitoring system is tested on a set of two hundred forty people. The simulation results are also compared with well-known techniques available for diabetes prediction. It is stated that proposed monitoring system obtains 90.41% accuracy rate as compared with other techniques.

Healthcare functionality is enriched by cloud services which offers a perspective for broad integration and interoperability. Cloud-based facilities support healthcare systems to remain connected to... Show More

Healthcare functionality is enriched by cloud services which offers a perspective for broad integration and interoperability. Cloud-based facilities support healthcare systems to remain connected to remote access devices to various tasks and information. The healthcare actors should have an understanding of the risks and benefits associated with the usage of Cloud Computing resources utilization. Also, they must launch an appropriate contract-based relationship between the Cloud Service Providers and the actors of healthcare systems by means of Service Level Agreements (SLAs). The variation in both demand and supply within the healthcare information affects the use of information technology. Hence, monitoring resources can play an important role in accommodating the healthcare data. To deal with the aforementioned problems; reinforcement learning mechanisms along with the metrics has been used and experimented with the various dynamics of workload to deliver services with quality assurance.

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners.... Show More

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.

This article presents a qualitative analysis of a 3D routing algorithm in a 3×3×3 mesh NOC topology. The effect of load variation on throughput, total energy, and maximum delay for different types... Show More

This article presents a qualitative analysis of a 3D routing algorithm in a 3×3×3 mesh NOC topology. The effect of load variation on throughput, total energy, and maximum delay for different types of routing is observed. The simulation was performed on an Access NOXIM network-on-chip simulator under random traffic conditions. The research involves quality parameters like total packets received, total received flits, global average delay (cycles), global average throughput (flits/cycle), throughput (flits/cycle/IP), max delay (cycles), total energy (J), average power (J/cycle), average power per router (J/cycle), and average waiting time in each buffer. In this article, it was observed after comparing all the routing techniques against the mention parameters the XYZ routing techniques was found perform better followed by West first, and North last, while poor performance was observed against odd-even, negative first, and fully adaptive.