Journal•ISSN: 2228-6179
Iranian Journal of Science and Technology-Transactions of Electrical Engineering
Springer Science+Business Media
About: Iranian Journal of Science and Technology-Transactions of Electrical Engineering is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Control theory. It has an ISSN identifier of 2228-6179. Over the lifetime, 632 publications have been published receiving 3835 citations. The journal is also known as: Electrical and computer engineering & Iranian journal of science and technology.
Topics: Computer science, Control theory, Control theory (sociology), Electric power system, Nonlinear system
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TL;DR: This paper proposes a secure and efficient user authentication scheme for remote patient monitoring that is robust, lightweight and secure against multiple security attacks and has low computational overhead.
Abstract: With the ongoing revolution of cloud computing and Internet of Things, remote patient monitoring has become feasible. These networking paradigms are widely used to provide healthcare services and real-time patient monitoring. The sensors that are either wearable or embedded within the body of a patient transmit patient’s data to the remote medical centers. The medical professional can access patient’s data stored in the cloud anywhere across the globe. As the sensitive data of the patient are sent over insecure cloud-IoT networks, secure user authentication is of utmost importance. An efficient user authentication scheme ensures that only legitimate users can access data and services. This paper proposes a secure and efficient user authentication scheme for remote patient monitoring. The proposed scheme is robust, lightweight and secure against multiple security attacks. Furthermore, the scheme has low computational overhead. A formal verification using AVISPA tool confirms the security of the proposed scheme.
93 citations
TL;DR: In this article, three different CNN models are proposed for three different classification tasks, i.e., classification of brain tumor MRI images using grid search optimization algorithm, which achieved 99.33% accuracy with the first CNN model and 92.66% with the second CNN model.
Abstract: Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.
88 citations
TL;DR: In this method, a new formula is developed to evaluate the criterion weights, in which the objective weights are calculated from divergence measure method, which can be a useful tool for decision making in an uncertain atmosphere.
Abstract: In this manuscript, we present complex proportional assessment (COPRAS) method to solve multi-criteria decision-making (MCDM) problems with intuitionistic fuzzy information, known as IF-COPRAS method. In this method, a new formula is developed to evaluate the criterion weights, in which the objective weights are calculated from divergence measure method. For this, new parametric divergence and entropy measures are investigated and some desirable properties are also discussed. Since the vagueness or uncertainty is an unavoidable characteristic of MCDM problems, the proposed approach can be a useful tool for decision making in an uncertain atmosphere. Further, a decision-making problem of green supplier selection is presented to demonstrate the usefulness of the proposed method. To illustrate the validity of the proposed method, comparison with existing methods is presented and the stability is also discussed through a sensitivity analysis with different values of criterion weights.
87 citations
TL;DR: The theme of this work is to present some new operational laws for intuitionistic fuzzy numbers and their averaging and geometric aggregation operators under the completely unknown attribute weights.
Abstract: The theme of this work is to present some new operational laws for intuitionistic fuzzy numbers and their averaging and geometric aggregation operators under the completely unknown attribute weights. To accomplish this, firstly the shortcoming of the existing operations has been highlighted, and then, they have been mitigated by defining intuitionistic fuzzy Hamacher interaction weighted averaging and geometric aggregation operators by considering the pairs of membership functions. Some of the desirable properties of the proposed operators are stated. The attribute weight vector used for aggregating the decision maker’s preferences has been computed by using the entropy function. Finally, a decision-making approach has been presented and illustrated with a numerical example to demonstrate the superiority of the approach over the existing operators.
68 citations
TL;DR: An exhaustive survey has been carried out, considering both the general purpose and satellite images to cover the performance comparison of various image segmentation approaches based on meta-heuristics optimization algorithms, present in the literature for multilevel image thresholding.
Abstract: Image segmentation is a basic problem in computer vision and various image processing applications. Over the years, commonly used image segmentation has become quite challenging because of its utilization in many applications. Image thresholding is one of the most exploited techniques to accomplish image segmentation. Multilevel thresholding is found to be most appropriate and well known among all the image segmentation techniques. The segmented image quality is based on the techniques incorporated to choose the threshold value. In this paper, an exhaustive survey has been carried out, considering both the general purpose and satellite images to cover the performance comparison of various image segmentation approaches based on meta-heuristics optimization algorithms, present in the literature for multilevel image thresholding. In addition, this paper also focuses on information theoretic approach-based objective criterion using different statistical properties such as between-class variance, entropy, moment and maximum likelihood for selecting multilevel thresholds. A list of 157 publications on the subject is also appended for quick reference.
66 citations