Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

A MAS-Based Approach for POI Group Recommendation in LBSN

International audienceLocation-based recommender systems (LBRS) suggest friends, events, and places considering information about geographical locations. These recommendations can be made to individuals but also to groups of users, which implies satisfying the group as a whole. In this work, we analyze dierent alternatives for POI group recommendations based on a multi-agent system consisting of negotiating agents that represent a group of users. The results obtained thus far indicate that our multi-agent approach outperforms traditional aggregation approaches, and that the usage of LBSN information helps to improve both the quality of the recommendations and the eciency of the recommendation process

Efficient Authentication of Approximate Record Matching for Outsourced Databases

Cloud computing enables the outsourcing of big data analytics, where a third-party server is responsible for data management and processing. A major security concern of the outsourcing paradigm is whether the untrusted server returns correct results. In this paper, we consider approximate record matching in the outsourcing model. Given a target record, the service provider should return all records from the outsourced dataset that are similar to the target. Identifying approximately duplicate records in databases plays an important role in information integration and entity resolution. In this paper, we design ALARM, an Authentication soLution of outsourced Approximate Record Matching to verify the correctness of the result. The key idea of ALARM is that besides returning the similar records, the server constructs the verification object (VO) to prove their authenticity, soundness and completeness. ALARM consists of four authentication approaches, namely V S 2 , E-V S 2 , G-V S 2 and P-V S 2 . These approaches endeavor to reduce the verification cost from different aspects. We theoretically prove the robustness and security of these approaches, and analyze the time and space complexity for each approach. We perform an extensive set of experiment on real-world datasets to demonstrate that ALARM can verify the record matching results with cheap cost