Journal of Trends in Computer Science and Smart Technology - September 2019
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Published By Inventive Research Organization

Updated Wednesday, 20 October 2021

Haoxiang Wang

In recent days the need for energy resources is dramatically increasing world-wide. Overall 80% of the energy resource is supplied in the form of fuel based energy source and nuclear based energy source. Where fuel based energy resources are very essential in day-to-day life. Fossil fuel is also one among the energy resource and due to the high demand we face shortage in these resources. Providing electricity in rural areas is still a difficult process because of the shortage of energy resources. This issue can be rectified by choosing an alternate to electricity. To achieve this we have integrated many renewable energy sources to form a hybrid-renewable energy source system and this is capable of providing power supply to these areas. We have adopted artificial neural networks (ANN) technique based on machine learning to accomplish this process. For short-term prediction other techniques such as MLP, CNN, RNN and LSTM are used. These values are used as reference value in final execution.

C Anand

Two important paradigms which are contradicting by nature namely: the efficient routing information diffusion and adaptability to dynamic network conditions using wireless routing protocols have been researched in recent years. One way of solving this issue is by using the past experiences of a node in network traffic condition through intelligent algorithm to predict the network traffic condition in the future. In this methodology we propose an algorithm which is used to to predict one hop delay per packet during routing process using neural networking. The one hop delay that is predicted is then further used by the participating nodes for information diffusion during routing. Experimental analysis indicate that using tapped delay line radial basis function and tapped delay line multilayer perceptron, it is possible to predict mean delays as a time series. The inputs used for prediction are mean delay time series with traffic loads and mean delay time series itself. The pros and cons of the proposed work are also present in this paper.

J. Samuel Manoharan

Forgeries have recently become more prevalent in the society as a result of recent improvements in media generation technologies. In real-time, modern technology allows for the creation of a forged version of a single image obtained from a social network. Forgery detection algorithms have been created for a variety of areas; however they quickly become obsolete as new attack types exist. This paper presents a unique image forgery detection strategy based on deep learning algorithms. The proposed approach employs a convolutional neural network (CNN) to produce histogram representations from input RGB color images, which are then utilized to detect image forgeries. With the image separation method and copy-move detection applications in mind, the proposed CNN is combined with an intelligent approach and histogram mapping. It is used to detect fake or true images at the initial stage of our proposed work. Besides, it is specially designed for performing feature extraction in image layer separation with the help of CNN model. To capture both geographical and histogram information and the likelihood of presence at the same time, we use vectors in our dynamic capsule networks to detect the forgery kernels from reference images. The proposed research work integrates the intelligence with a feature engineering approach in an efficient manner. They are well-known and efficient in the identification of forged images. The performance metrics such as accuracy, recall, precision, and half total error rate (HTER) are computed and tabulated with the graph plot.

Subarna Shakya

Deep learning methods have gained an increasing research interest, especially in the field of image denoising. Although there are significant differences between the different types of deep learning techniques used for natural image denoising, it includes significant process and procedure differences between them. To be specific, discriminative learning based on deep learning convolutional neural network (CNN) may effectively solve the problem of Gaussian noise. Deep learning based optimization models are useful in predicting the true noise level. However, no relevant research has attempted to summarize the different deep learning approaches for performing image denoising in one location. It has been suggested to build the proposed framework in parallel with the previously trained CNN to enhance the training speed and accuracy in denoising the Gaussian White Noise (GWN). In the proposed architecture, ground truth maps are created by combining the additional patches of input with original pictures to create ground truth maps. Furthermore, by changing kernel weights for forecasting probability maps, the loss function may be reduced to its smallest value. Besides, it is efficient in terms of processing time with less sparsity while enlarging the objects present in the images. As well as in conventional methods, various performance measures such as PSNR, MSE, and SSIM are computed and compared with one another.

G. Christina

Antennas are metallic structure elements developed for transmitting signals through radio waves. Nowadays, antennas are available in different shape depending upon their application and signal strength. The antennas which are employed for space and large signal communication utilizes a bowl shape structure for focusing the signals on a single point. Certain antennas are designed to move on both horizontal and vertical directions for their signal transmission. The microstrip patch antennas are very small in size and it comes under the type of printed antennas. The microstrip patch antennas are widely employed on mobile phone communications and medical applications. The performances of the microstrip patch antennas are increased in recent years and the motive of the review work is to analyse the methodology followed behind it. In the same way, the work analyses the merits and limitations of the recent techniques developed for the performance improvement of the microstrip patch antennas.

Joy Iong-Zong Chen ◽  
Kong-Long Lai

Stochastic Geometry has attained massive growth in modelling and analysing of wireless network. This suits well for analysing the performance of large scale wireless network with random topologies. Analytical framework is established to evaluate the performance of the network. Here we have created a mathematical model for uplink analysis and the gain of uplink and downlink is obtained. Then ad-hoc network architecture is designed and the performance of the network is compared with the traditional method. Finally, a new scheduling algorithm is developed for cellular network and the gain parameter is quantified with the help of Stochastic Geometry tool. The accuracy is acquired from extensive Monte Carlo simulator.

Suma V

The Internet of Things [IoT] is one of the most recent technologies that has influenced the way people communicate. With its growth, IoT encounters a number of challenges, including device heterogeneity, energy construction, comparability, and security. Energy and security are important considerations when transmitting data via edge networks and IoT. Interference with data in an IoT network might occur unintentionally or on purpose by malicious attackers, and it will have a significant impact in real time. To address the security problems, the suggested solution incorporates software defined networking (SDN) and blockchain. In particular, this research work has introduced an energy efficient and secure blockchain-enabled architecture using SDN controllers that are operating on a novel routing methodology in IoT. To establish communication between the IoT devices, private and public blockchain are used for eliminating Proof of Work (POW). This enables blockchain to be a suitable resource-constrained protocol for establishing an efficient communication. Experimental observation indicates that, an algorithm based on routing protocol will have low energy consumption, lower delay and higher throughput, when compared with other classic routing algorithms.

Joy Iong-Zong Chen ◽  
S Smys

In recent years, both developed and developing countries have witnessed an increase in the number of traffic accidents. Aside from a significant rise in the overall number of on-road commercial and non-commercial vehicles, advancements in transportation infrastructure and on-road technologies may result in road accidents, which generally result in high mortality. More than half of these fatalities are the result of delayed response by medical and rescue personnel. If an accident site receives quick medical treatment, an accident victim's chances of survival may improve considerably. Based on the IoT-based multiple-level vehicle environment, this study proposes a low-cost accident detection and alarm system. Vehicles are equipped with a "Black Box" board unit and an accident location identification module for the Global Positioning System (GPS), in addition to mechanical sensors (accelerometer, gyroscope) for accurate accident detection. This study has evaluated the proposed system with average packet delivery ratio (PDR) vs. relay nodes. Our simulation results have evaluated the evolution of relay nodes in the mobile / sensor node through internet gateway. It has also been demonstrated that the packet delivery ratio is inversely related to the incremental number of relay nodes.

R Dhaya

The automated captioning of natural images with appropriate descriptions is an intriguing and complicated task in the field of image processing. On the other hand, Deep learning, which combines computer vision with natural language, has emerged in recent years. Image emphasization is a record file representation that allows a computer to understand the visual information of an image in one or more words. When it comes to connecting high-quality images, the expressive process not only requires the credentials of the primary item and scene but also the ability to analyse the status, physical characteristics, and connections. Many traditional algorithms substitute the image to the front image. The image characteristics are dynamic depending on the ambient condition of natural photographs. Image processing techniques fail to extract several characteristics from the specified image. Nonetheless, four properties from the images are accurately described by using our proposed technique. Based on the various filtering layers in the convolutional neural network (CNN), it is an advantage to extract different characteristics. The caption for the image is based on long short term memory (LSTM), which comes under recurrent neural network. In addition, the precise subtitling is compared to current conventional techniques of image processing and different deep learning models. The proposed method is performing well in natural images and web camera based images for traffic analysis. Besides, the proposed algorithm leverages good accuracy and reliable image captioning.

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