AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features.

The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.

Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions.

Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.

Ontology Driven Smart Health Service Integration.

BACKGROUND AND OBJECTIVE: The massive increase, in the Internet of Things applications, has greatly evolved technological aspects of human life. The drastic development of IoT based smart healthcare services have layout the smart process models to facilitate all stakeholders (e.g. patients, doctors, hospitals etc.) and made it an important social-economic concern. There are variety of smart healthcare services like remote patient monitoring, diagnostic, disease specific remote treatments and telemedicine. Many trending Internet of Health Things research and development are done in a very disjoint and independent fashion providing solutions and guidelines for variant diseases, medical resources and remote services management. These expositions work over many shared resources such as health facilities for patient and human in healthcare system. METHODS: This research discusses the ontology for merging methods to form an integrated platform with shared knowledge of smart healthcare services. The proposed process model creates an ontological framework of integrated healthcare services, which are firstly defined using ontologies and lately integrated over similarities, differences, dependencies and other semantic relations. The data and process requirements for service integration facility is derived from various smart healthcare services. RESULTS: The proposed model is evaluated using two-step ontological modeling testing method, applied at the ontological framework of integrated smart health services. First evaluation step has targeted the model consistency validation using reasoning tool while querying tools are used to validate the retrieved data entities and relations among them for predefined use-cases. CONCLUSIONS: The research concluded with a novel approach for smart health service integration using ontological modeling and merging techniques. The model efficiency enhancement and query optimization methods are listed in future tasks of the research.