Dileep Kumar Singh

One of the most actively studied topics in modern medicine is the use of deep learning and patient clinical data to make medication and ADR recommendations. However, the clinical community still has some work to do in order to build a model that hybridises the recommendation system. As a social media learning based deep auto-encoder model for clinical recommendation, this research proposes a hybrid model that combines deep self-decoder with Top n similar co-patient information to produce a joint optimisation function (SAeCR). Implicit clinical information can be extracted using the network representation learning technique. Three experiments were conducted on two real-world social network data sets to assess the efficacy of the SAeCR model. As demonstrated by the experiments, the suggested model outperforms the other classification method on a larger and sparser data set. In addition, social network data can help doctors determine the nature of a patient's relationship with a co-patient. The SAeCR model is more effective since it incorporates insights from network representation learning and social theory.

One of the rising fields of research are hand gesture recognition. Being an important part of non-verbal interaction is a critical part of our everyday life. Hand management recognition systems allow us to communicate with the machine, which is more familiar to the human being, creative, normal, user-friendly. Hand gesture recognition covers a wide range of applications including contact with the human machine, sign language, immersive game technology etc. To perform this we have implemented a method of hand recognition and extraction of features by means of a web camera in real time. The picture is captured in this method through the system's webcam. The initial processing of the input image and thresholding are used to remove image noise and smooth the image. Following this, apply region filling in the gesture or object of interest to fill holes. The “HU moments” are used for feature extraction and classification using KNN algorithm. This helps in improving the classification though machine learning and recognition step. We have tested 43 gestures which include 26 gestures of English alphabets and achieved a confidence of 99.9%.

Intrusion Detection System (IDS) detects the intrusions and produces alerts. Automated Intrusion Response System (AIRS) selects and triggers the appropriate response based on some criteria to mitigate the intrusion without delay. The big challenges in the automated response selection process are a precise measurement of importance weight for each criterion and response prioritization for the specific category of attacks. Analytic hierarchy process (AHP) uses the pair-wise comparison of each criterion and does not require the accurate quantification but is unable to handle the vagueness or uncertainty in the importance judgment. This paper presents the framework called Fuzzy Rule-Based Automatic Intrusion Response Selection System (FRAIRSS) for automated response selection. Fuzzy AHP model has been created in order to deal with precise measurement and uncertainty in the importance judgment of each criterion. Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multi-criteria decision making (MCDM) approach has been applied in order to resolve the response prioritization. Fuzzy Rule-based inference system is modeled to select the appropriate response from the prioritized response sets for each category of attacks. The framework has been simulated in MATLAB with various attack scenarios and it is found that FRAIRSS is selecting most appropriate response under the given attack scenarios.