Background: Cloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. Methods: Howe...
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Background: Cloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. Methods: However, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using rideroptimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. Results: The efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10 %, 20 %, and 35 % than OHE, HE and ECC algorithms.
Heart disease detection through early-stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing ...
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Heart disease detection through early-stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm-based deep residual network (SWCA-based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimizationalgorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm-based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimizationalgorithm, named SWCA. Furthermore, developed SWCA-enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925.
Rice diseases have degraded the production of the rice plant, which produces economic loss. To control and minimize the effects of attacks, the diseases are required to be recognized at a premature stage. Premature de...
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Rice diseases have degraded the production of the rice plant, which produces economic loss. To control and minimize the effects of attacks, the diseases are required to be recognized at a premature stage. Premature detection of infections can improve the yield from quantitative as well as qualitative losses, diminish the usage of pesticides, and improve the economic growth of the country. Hence, this paper devises a new method, namely rider Water Wave-based neural network (RWW-NN) for finding the disease in the rice plant, where the training of NN is completed using the RWW, which is formed by assimilating rider optimization algorithm (ROA) and Water wave optimization (WWO). Initially, the pre-processing is done by using histogram equalization from the input image. Then, the segmentation is completed using Segmentation Network (SegNet), and then the CNN features are employed for feature mining in order to acquire the optimal features for disease recognition. These features are fed to NN for disease detection wherein the RWW is introduced for training the optimal weights. The RWW-based NN acquires greatest accuracy of 0.908, F-measure of 0.907, sensitivity of 0.862, and specificity of 0.947 based on K-value using Rice disease dataset.
An affective state of a learner in E-learning has gained enormous interest. The prediction of the emotional state of a learner can enhance the outcome of learning by including designated mediation. Many techniques are...
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An affective state of a learner in E-learning has gained enormous interest. The prediction of the emotional state of a learner can enhance the outcome of learning by including designated mediation. Many techniques are developed for anticipating emotional states using video, audio, and bio-sensors. Still, examining video, and audio will not confirm secretiveness and is exposed to security issues. Here the creator devises a fusion technique, to be specific Squirrel Search and rideroptimization-grounded Deep LSTM for affect prediction. The Deep LSTM is trained to exercise the new fusion SS-ROA. Then, the SS-ROA-grounded Deep LSTM classifies the states like frustration, confusion, engagement, wrathfulness, and so on. It is based on the interaction log data of the E-learner. In conclusion, the course and student ID, predicted state, test marks, and course completion status are taken as result information to find out the correlations. The new algorithm gives the best performance in comparison to other present methods with the highest prediction accurateness of 0.962 and the most noteworthy connection of 0.379 respectively. After discovering affective states, students may get the advantage of getting real comments from a teacher for improving one's performance during learning. However, such systems should also give feedback about the learner's affective state or passion because it greatly affects the student's encouragement toward better learning.
Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massiv...
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Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional rider-Harmony search algorithm (Fractional rider-HSA) for resource allocation in the cloud. The Fractional rider-HSA combines fractional calculus, rider optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.
ABSTRACTCurrently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous se...
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ABSTRACTCurrently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data; therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the rider Horse Herd optimizationalgorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the rider optimization algorithm (ROA) and Horse herd optimizationalgorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.
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