Narrow Results

One of the emerging technologies, seeking significant attention in the research area is cloud computing. However, privacy is the major concern in the cloud, as it is essential to manage the confidentiality in the data shared. In the first work, the privacy preservation model was developed by newly designed Kronecker product based Bat algorithm. Here, the previous work is extended by developing the classification algorithm for classifying the privacy preserved database. Initially, the Kronecker product based Bat algorithm finds the privacy preserved database from the original medical data. Then, the ontology based features are extracted from the privacy preserved database and given to the data classifier. Here, a classifier, named Whale based Sine Cosine Algorithm with Support Vector Neural Network (WSCA-SVNN), is newly developed for the data classification. The proposed WSCA algorithm helps in optimally choosing the weights for SVNN classifier, and finally, the WSCA-SVNN classifier classifies the medical data. The simulation of the proposed privacy preserved data classification network is done by utilizing the heart disease database. The analysis shows that the proposed WSCA-SVNN classifier scheme achieved an accuracy value of 90.29% during medical data classification.

In today’s world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (E_c), Duration of BH attack detection (Attduration), Packet delivery ratio (P_dr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%.

Managing medical information in a Big Data context is a challenging task since searching for relevant information in a large volume of data needs advanced treatments. Medical data is a special type of data because it comes from different sources and in different formats and encapsulates medical knowledge. Personalized retrieval is necessary when it comes to medical data management. In fact, the patient’s medical record needs to be taken into account in order to offer relevant documents since it contains his/her medical history. The proposed approach offers an ontology building process based on the patient’s medical record. The built ontology is then used for personalized information retrieval as well as user similarity computation. The approach is composed of three layers: (1) Data layer, (2) Treatment layer and (3) Semantic layer and offers three treatments: (1) Ontology building, (2) Query reformulation and (3) User similarity computation. An application supporting all three layers has been implemented and it allowed an experimental evaluation of the proposal. The results show an improvement in the relevancy of returned medical documents.

In the medical field, ontologies are mainly used to standardise the coding of knowledge, either during the drafting phase of documents or during subsequent processing intended to give them a format which makes them usable for automatic processing. In this sense, they have a normative role analogous to the classical medical terminologies (in particular thesauri): to set up a common vocabulary and to make use of shared representations and concepts, in order to allow the interoperability of documents and medicals information systems. Medical and biomedical data play a very important role in the medical field. They solve many of the problems encountered during the diagnosis of diseases and in the prescription medical treatments. The objective of this paper is to develop ontology for the field of medical analyses in an attempt to facilitate the tasks of the various actors in the medical field, namely: doctors, laboratory assistants and patients. It also endeavours to improve the care of patients and facilitate the various acts of health professionals, the purpose of which is to aid in decision-making in the treatment of a disease. To this end, we followed the so-called METHONTOLOGY based on the phases ranging from the needs specification to the evaluation and documentation of this ontology of medical analysis.

In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of $\mathrm{\Theta }$ and normalization. In order to evaluate the theta ($\mathrm{\Theta }$) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

Traditional skin cancer screening necessitates a time-consuming physical examination by a dermatologist. One of the most difficult tasks in image analysis is automated medical picture recognition and categorization. Making a skin-based analysis to predict the abnormalities present in a particular area is always a competitive issue. In the case of automatic region identification, deep learning (DL) methodologies play a major role. Most DL approaches are unable to offer uncertainty quantification (UQ) for the output, resulting in overconfidence and excessive sample deviation. The dataset was contributed by the International Skin Image Collaboration (ISIC) Archive. To deal with uncertainty during skin cancer image classification and disappearing gradients, we used Monte-Carlo (MC) dropout with the ResNet 50 DL model. This assists in overcoming the problem of vanishing gradients. After the preprocessing, the training was completed, yielding a 94% accuracy rate. The MC dropout method identifies the out-of-domain data, reduces the training size which results in an accuracy of 89.9%. As a result, the ResNet50 model for classifying skin cancer achieved 89% of accuracy. In addition, our model was in general robust to noise and performed well with previously unseen data.