Volume 12, Number 3

Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16.  It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.

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The researcher have suggested real time depth based on frequency domain hole filling. It get better quality of depth sequence generated by sensor. This method is capable to produce high feature depth video which can be quite useful in improving the performance of various applications of Microsoft Kinect such as obstacle detection and avoidance, facial tracking, gesture recognition, pose estimation and skeletal. For stereo matching approach images depth extraction is the hybrid (Combination of Morphological Operation)  mathematical algorithm. There are few step like color conversion, block matching, guided filtering, minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last nonlinear spatial filtering. Our algorithm is produce smooth, reliable, noise less and efficient depth map. The evaluation parameter such as Structure Similarity Index Map (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) measure the results for proportional analysis.

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Body modification (or body alteration) is the wilful altering of the human body by an individual in a way that lasts forever or for a very long time. This is usually for non-medical reasons that include sexual enhancement, a rite of passage, aesthetic reasons, denoting affiliation, trust and loyalty, religious reasons, shock value, and self-expression. It can range from the socially acceptable decoration (e.g., pierced ears or nose in many societies) to the religiously mandated. Body art is the modification of any part of the human body for artistic or aesthetic reasons.  Nanotechnology is currently available to implant biometric devices in human beings, which can be monitored by software, satellites and utilized by Government and Industry. In fact several developers are currently bringing these technologies to the public and private sector at affordable prices.  The context of “Technology Consumerism” compounded by Intentionality and Free-Will of its consumer’s results in many unintended consequences outlined in this paper. Geometry of Morphogenesis is the proposed theory for decoding body modification.

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E-learning system is designed to aid students build their comprehension towards their respected academic subjects to make their experience in learning more nourishing and engaging. The researchers decided to conduct this study to be au fait the benefits of online education and how the students accept the change this innovation gave. A survey questionnaire was distributed to the enrolled college students of AMA Computer College Year 2018-2019 to make the research more reliable, accurate and at the same time tackle the benefits of the said innovated education system. The said research was a success in defining what helps the students in their education as well as the improvement it can do in terms of catering different subjects and assessing the learners’ competency. The researchers recommend the future researchers to further expand and elaborate the topic in order to enhance the study. To further extend the scope and distribute further information regarding the tackled study.

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In 1943 Machculloch and Pitts create the formal neuron where many input signals are linearly composed with different weights on the neuron soma. When the soma electrical signal goes over a specific threshold an output is produced. The main topic in this model is that the response is the same response as in a Boolean function used a lot for the digital computer. Logic functions can be simplified with the formal neuron. But there is the big problem for which not all logic functions, as XOR , cannot be designed in the formal neuron. After a long time the back propagation and many other neural models overcame the big problem in some cases but not in all cases creating a lot of uncertainty. The model proposed does not consider the formal neuron but the natural network controlled by a set of differential equations for neural channels that model the current and voltage on the neuron surface. The steady state of the probabilities is the activation state continuous function whose maximum and minimum are the values of the Boolean function associated with the activation time of spikes of the neuron. With this method the activation function can be designed when the Boolean functions are known. Moreover the neuron differential equation can be designed in order to realize the wanted Boolean function in the neuron itself. The activation function theory permits to compute the neural parameters in agreement with the intention.

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One of the biggest problems in using artificial neural networks is to determine the best architecture. This is a crucial problem since there are no general rules to select the best architecture structure. Selection of the best architecture is to determine how many neurons should be used in the layers of a network. It is a well-known fact that using a proper architecture structure directly affect the performance of the method. Therefore, various approaches ranging from trial and error method to heuristic optimization algorithms have been suggested to solve this problem in the literature. Although there have been systematical approaches in the literature, trial and error method has been widely used in various applications to find a good architecture. This study propose a new architecture selection method based on statistical and machine learning. The proposed method utilizes regression analysis that is a supervised learning technique in machine learning. In this new architecture selection approach, it is aimed to combine statistical and machine learning to reach good architectures which has high performance. The proposed approach brings a new perspective since it is possible to perform statistical hypothesis tests and to statistically evaluate the obtained results when artificial neural networks are used. The best architecture structure can be statistically determined in the proposed approach. In addition to this, the proposed approach provides some important advantages. This is the first study using a statistical method to utilize statistical hypothesis tests in artificial neural networks. Using regression analysis is easy to use so applying the proposed method is also easy. And, the proposed approach saves time since the best architecture is determined by regression analysis. Furthermore, it is possible to make inference for architectures which is not examined. The proposed approach is applied to three real data sets to show the applicability of the approach. The obtained results show that the proposed method gives very satisfactory results for real data sets.

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Systematic pursuits are being developed to set forth the framework for the Fifth Generation (5G) wireless standards. This paper emphases on the most extensively deployed technology - Orthogonal Frequency Division Multiplexing (OFDM) that has outpaced other waveform aspirants for Fourth Generation (4G) communication standards. Irrespective of the beneficial features, it does possess a number of significant limitations that mark it as an incompatible candidate for the upcoming 5G standard. This paper highlights on its major drawback i.e high Peak-to-Average Power Ratio (PAPR). Results state that PAPR does cause sudden upsurge to the output signal envelope causing further other damages. There exists a need for more flexible waveforms to replace the conventional OFDM in order to address the unprecedented challenges. The future research directions in the domain are presented.

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