Under the Digital Image and Communication in Medicine (DICOM) standard, the Advanced Encryption Standard (AES) is used to encrypt medical image pixel data. This highly sensitive data needs to be transmitted securely over networks to prevent data modification. Therefore, there is ongoing research into how well encryption algorithms perform on medical images and whether they can be improved. In this paper, we have developed an algorithm using a chaotic map combined with AES and tested it against AES in its standard form. This comparison allowed us to analyse how the chaotic map affected the encryption quality. The developed algorithm, CAT-AES, iterates through Arnold’s cat map before encryption a certain number of times whereas, the standard AES encryption does not. Both algorithms were tested on two sets of 16-bit DICOM images: 20 brain MRI and 26 breast cancer MRI scans, using correlation coefficient and histogram uniformity for evaluation. The results showed improvements in the encryption quality. When encrypting the images with CAT-AES, the histograms were more uniform, and the absolute correlation coefficient was closer to zero for the majority of images tested on.

Translation has become very important in our generation as people with completely different cultures and languages are networked together through the Internet. Nowadays one can easily communicate with anyone in the world with the services of Google Translate and/or other translation applications. Humans can already recognize languages that they have priory been exposed to. Even though they might not be able to translate, they can have a good idea of what the spoken language is. This paper demonstrates how different Neural Network models can be trained to recognize different languages such as French, English, Spanish, and German. For the training dataset voice samples were choosed from Shtooka, VoxForge, and Youtube. For testing purposes, not only data from these websites, but also personally recorded voices were used. At the end, this research provides the accuracy and confidence level of multiple Neural Network architectures, Support Vector Machine and Hidden Markov Model, with the Hidden Markov Model yielding the best results reaching almost 70 percent accuracy for all languages.

In future heterogeneous network communication systems, driven by the evolution of today’s most demanding applications, resource allocation and its mining will play an increasingly significant role in our daily life for different use of applications facing a rapid growth in data traffic demands recently. In this work based on future network architecture, we proposed a scheme called Optimization of Resource Mining in Distributive Sharing (ORMDS) for better utilization of in-house residual buffer to improve performance of the network in-terms of maximizing the efficiency, minimizing network delay, call drop and buffer mining. A distributed buffer allocation and mining in the framework is proposed to facilitate different multimedia application in future network. Resource is measured and updated in the buffer table of router. Buffer table is updated based on execution of optimization computation model, which uses optimization seeking model for determining the value of objective function. This minimum value of objective function will improve the resource utilization of the network. The proposed scheme reduces the mining time of the buffer in sharing system and optimizes the delay by using distributed sharing option. This ultimately gives optimum resource sharing at a different instant of time.

The perfect alignment between three or more sequences of Protein, RNA or DNA is a very difficult task in bioinformatics. There are many techniques for alignment multiple sequences. Many techniques maximize speed and do not concern with the accuracy of the resulting alignment. Likewise, many techniques maximize accuracy and do not concern with the speed. Reducing memory and execution time requirements and increasing the accuracy of multiple sequence alignment on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the most well-known programs (CLUSTAL-OMEGA, MAFFT, BROBCONS, KALIGN, RETALIGN, and MUSCLE). For programs’ testing and evaluating, benchmark protein datasets are used. Both the execution time and alignment quality are two important metrics. The obtained results show that no single MSA tool can always achieve the best alignment for all datasets.

One of the prominent challenges for offering seamless communication system while performing vertical handover in heterogeneous network is to relay the communication without identifying the accurate demands of the resources as well as quality of services for the newly moved node. After reviewing the existing literatures, it was found that there is a potential research gap in addressing this problem of seamless vertical handover. Therefore, the proposed manuscript addresses this problem by introducing a novel analytical model which is capable of formulating a precise decision for controlling the selection /dropping of the data packets on the basis of dynamic state of the network condition. The proposed system contributes faster processing by arbitrarily selecting the packets to be forwarded with a very unique and simple resource management. The study outcome of proposed system highlights an increased throughput and reduced length of queue along with better fairness control to offer seamless vertical handover.

Road accidents, besides being one of the main causes of mortality, have an economic impact on vehicle owners. Several conditions as driver imprudence, road conditions and obstacles are the main factor that will cause accidents. The most important automotive industries are incorporating technology to reduce risk in vehicles. In this way, lane detection systems have important attention, because from this data is possible to determine risk situations such as presence of obstacles, incorrect lane changes or lane departures. This paper proposes a technique for lane detection, based on image processing, which allows identifying the position of lateral lanes and their type. The method is composed of four stages: edge enhancement, potential lanes detection, post-processing and color lane estimation. The method was proved using image dataset and video captures over 12.000 frames. The accuracy of the system was of 91.9%.

Data mining is a descriptive and predictive data analytical technique that discovers meaningful and useful knowledge from dataset. Clustering is one of the descriptive analytic techniques of data mining that uses latent statistical information that exists among dataset to group them into meaningful and or useful groups. In clinical decision making, information from medical tests coupled with patients’ medical history is used to make recommendations, and predictions. However, these voluminous medical datasets analysis is always dependent of individual analyzer that might have in one way or the other introduced human error. In other to solve this problem, many automated analyses have been proposed by researchers using various machine learning techniques and various forms of dataset. In this paper, dataset from electrical impedance imaging of breast tissues are clustered using two unsupervised algorithms (k-means and self-organizing map). Result of the performances of these machine learning algorithms as implemented with R i368 version 3.4.2 shows a slight outperformance of K-means in terms of classification accuracy over self-organizing map for the considered dataset.

The world is going to be a universal digital village and from the flow of this digitalization Bangladesh also riding of the tide. The key points of these digitalization is young generations basically university students of Bangladesh. ICT and Internet is a new trend for this country that’s the main reasons to encompass this by opportunity by young students. Bangladeshi young generations have also addicted in the upper tier of this list. The addiction of ICT & internet is more on the young generations than any other parts of the people generally in the third world countries. The main objective of this paper is to investigate the excessive use of Information and Communication Technology (ICT) and internet by the university students in Bangladesh. The study had collected the data from 24 public and private universities in Bangladesh out of 135. The study was used the simple random sampling (SRS) for analyzing the sample size with IBM SPSS 23.