A novel image encryption scheme based on orthogonal matrix,skew tent map,and XOR operation

Neural Computing and Applications - Content protection is considered as an important issue in today’s world. Therefore, encryption of such contents is a challenging task for researchers. They...     

A computer-aided diagnostic system for liver tumor detection using modified U-Net architecture

The Journal of Supercomputing - A multimedia-based medical decision-making system is an ultimate requirement in the medical imaging domain. In the healthcare sector, achieving quick and efficient...     

Research on Sentiment Analysis of Network Forum Based on BP Neural Network

Nowadays, people pay more and more emotional to the emotional analysis of specific goals. Due to the long training time of many networks, this paper proposes a neural network with specific Objective sentiment analysis. Compared with the current neural network, the algorithm proposed in this paper has a shorter training time, which can effectively make up for the lack of emotional mechanism. Finally, we use the emotional data set to carry out simulation experiments. The experimental results show that the proposed algorithm is better than the ordinary neural network algorithm.

     

An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods.     

A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation (4G) and 5th generation (5G) etc. Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way. Instead of the classical random walk, this paper proposes the quantum walk to achieve high image security. Classical random walk exhibits randomness due to the stochastic transitions between states, on the other hand, the quantum walk is more random and achieve randomness due to the superposition, and the interference of the wave functions. The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient, entropy, histogram, time complexity, number of pixels change rate and unified average intensity etc. All experimental results validate the proposed scheme, and it is concluded that the proposed scheme is highly secured, lightweight and computationally efficient. In the proposed scheme, the values of the correlation coefficient, entropy, mean square error (MSE), number of pixels change rate (NPCR), unified average change intensity (UACI) and contrast are 0.0069, 7.9970, 40.39, 99.60%, 33.47 and 10.4542 respectively.     

Green waste to biogas: Renewable energy possibilities for Thailand's green markets

Today, the production of energy from waste is not a new process; however, its implementation and application continue to be a challenge in developing countries. Despite the abundance of valuable waste in the urban markets of these countries, practices aiming at renewable energy generation are missing. In Thailand, so-called green markets are replete with renewable energy potential, but the practical implementation of this potential is rare. Therefore, the main purpose of this study is to show that the conversion of green waste into renewable energy is not only environmentally beneficial but also financially rewarding. This is demonstrated by exploring the energy potential of the market and conducting a benefit–cost analysis under two scenarios. The results illustrate that for the selected market, converting organic waste into biogas is advantageous both environmentally as well as financially; further, the benefit–cost ratio is three times higher after conversion, compared to before. Additionally, there is a huge margin of conversion and production of biogas. The policy makers and planners of Talaad Thai (Thailand's largest green market) should invest greater effort in initiating plans, and set an example for other markets in Thailand, in order to make this planet clean and green.     

An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform,chaotic maps and substitution box

Wireless Sensor Networks (WSN) is widely deployed in monitoring of some physical activity and/or environmental conditions. Data gathered from WSN is transmitted via network to a central location for further processing. Numerous applications of WSN can be found in smart homes, intelligent buildings, health care, energy efficient smart grids and industrial control systems. In recent years, computer scientists has focused towards findings more applications of WSN in multimedia technologies, i.e. audio, video and digital images. Due to bulky nature of multimedia data, WSN process a large volume of multimedia data which significantly increases computational complexity and hence reduces battery time. With respect to battery life constraints, image compression in addition with secure transmission over a wide ranged sensor network is an emerging and challenging task in Wireless Multimedia Sensor Networks. Due to the open nature of the Internet, transmission of data must be secure through a process known as encryption. As a result, there is an intensive demand for such schemes that is energy efficient as well as highly secure since decades. In this paper, discrete wavelet-based partial image encryption scheme using hashing algorithm, chaotic maps and Hussain’s S-Box is reported. The plaintext image is compressed via discrete wavelet transform and then the image is shuffled column-wise and row wise-wise via Piece-wise Linear Chaotic Map (PWLCM) and Nonlinear Chaotic Algorithm, respectively. To get higher security, initial conditions for PWLCM are made dependent on hash function. The permuted image is bitwise XORed with random matrix generated from Intertwining Logistic map. To enhance the security further, final ciphertext is obtained after substituting all elements with Hussain’s substitution box. Experimental and statistical results confirm the strength of the anticipated scheme.     

Social media signal detection using tweets volume,hashtag, and sentiment analysis

Multimedia Tools and Applications - Social Media is a well-known platform for users to create, share and check the new information. The world becomes a global village because of the utilization of...     

Energy efficient watchman based flooding algorithm for IoT-enabled underwater wireless sensor and actor networks

In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I-UWSANs and cause high end-to-end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z-Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well-known algorithms like energy-aware scalable reliable and void-hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.     

Toward scalable cloud data center simulation using high-level architecture

Existing simulators are designed to simulate a few thousand nodes due to the tight integration of modules. Thus, with limited simulator scalability, researchers/developers are unable to simulate protocols and algorithms in detail, although cloud simulators provide geographically distributed data centers environment but lack the support for execution on distributed systems. In this paper, we propose a distributed simulation framework referred to as CloudSimScale. The framework is designed on top of highly adapted CloudSim with communication among different modules managed using IEEE Std 1516 (high-level architecture). The underlying modules can now run on the same or different physical systems and still manage to discover and communicate with one another. Thus, the proposed framework provides scalability across distributed systems and interoperability across modules and simulators.