Similar to decentralized communication systems, a new technology called Blockchain (BC) has the potential to store and manage data in a decentralized manner. By removing the role of third party, all member in the chain has equal access to data. The concept BCT (Blockchain Technology) is not just limited to the cryptocurrencies, but it has been implemented in other areas like e-health, voting, finance, education, smart contract and even in Databases. This paper discusses various Blockchain applications and platforms and then compares these platforms on basis of different parameters. Despite of the advantages, Blockchain faces a significant issue of privacy. This paper examines various security related issues and challenges and presents an account of known possible attacks.

Underwater Acoustic Sensor Networks offer very promising solutions to monitor the aqueous environments. Due to the distinctive characteristics of UASNs, it is very challenging to design a routing protocol that can achieve maximum data delivery ratio in the network. The main challenge is the communication medium (acoustic links) that is subject to temporary attenuation and high bit error rate (BER), which limits the throughput efficiency of the Network. Besides this, another major issue is the continuous movement of nodes due to water currents and the availability of limited resources. Due to nodes mobility distance among sensor nodes and consequently, BER varies, which have a direct impact on packet size, hence, leads to high packet loss and low data delivery ratio. To achieve a high data delivery ratio, the selection of optimal packet size is of utmost importance. Consequently, the selection of next-hop forwarding node based on optimal packet size is needed. Therefore, in this paper, we propose an adaptive routing protocol named Adaptive Packet Size Selection Based Routing (APSSR) Protocol for UASNs. APSSR determines the optimal packet size adaptively based on both varying distances between sensor nodes and BER and selects the next hop based on optimal packet size and BER. The simulation results show greater network performance in terms of Network Lifetime, Data Reception Ratio at Sink node, Average Network Delay, Packet Reception Ratio, and Packets Drop Ratio

Today’s age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both techniques play a significant role in measuring vulnerability prediction accuracy. In the field of computer security, vulnerability is a fault that might be exploited as a risk artist that performs unlawful activities inside computer security. The attackers have several different fitting tools and they are taking advantage to operate software illegally and are using it for getting self-profit. Additionally, that helps to expose and identify the violence external. Weakness management remains a repeating exercise to identify, remediating, and justifying weaknesses. These exercises normally send software faults in computing security. The meaning of using weakness with the same risk might go to misperception. It is possible to have a major effect because of possible stability and the window of weakness presented a risk hole in the software and required to fruitfully finish and smoothly operate. A security room has to be set up (zero-day invaders). Software Security Faults stand serious among unavoidable complications in the realm of computer risk. In this study, we have provided a comprehensive review of three book chapters, more than a hundred research articles papers, and several associated papers of different work that have been studied within the capacity of SVA and discovery applying ML and data-mining techniques. The earlier work has been thoroughly read and an adequately comprehensive summary has been provided in table-1. ML techniques that can professionally handle these attacks and we expect the net result of this survey article to help indesigning the new detection model for identifying the above-mentioned attacks

Lung Cancer is the most general type of disease in theworld ofcancer. It affects the lungs of the human body. So, the prediction of lung cancer at its earlier stage is difficult. It is the deadliest cause of death in both men and women. Its symptoms are harder to recognize in the initial stages.Machine learning algorithms have made the prediction and detection of lung cancereasier. Chi-square is used for feature selection to select the relevant features in the lung cancer dataset. Different Machine Learning algorithms are used to predict Lung Cancer.The algorithmsutilized in the proposed work are SVM and Random Forest. We have compared these algorithms with and without feature selection (Chi-square). SVM is identified as the best algorithm in the proposed work due to its accuracy and less execution time for detecting the model. The key objective of this paper is to enhance the accuracy and reduce the execution time of the classifier.

Authentication is a very important aspect of computer security. Most systems employ strategies such as Password-based authentication, Multi-factor authentication, Certificate based authentication which are accompanied with a lot ofchallenges. To address this issue, most security systems have introduced the use of biometrics for authentication. Unimodal biometrics systems have many limitations regarding performance and accuracy. The use of Multimodal biometrics systems for authentication is recently attracting the attention of researchers due to its capacity to overcome most of the drawbacks of Unimodal biometric systems. This paper focuses on the use of biometric technology for authentication. The strengths of multimodal biometric systems, together with the challenges of multimodal biometric systems are presented. The paper also suggests solutions to the challenges of multimodal biometric systems.

The global development and progress in scientific paraphernalia and technology is the fundamental reason for the rapid increasein the data volume. Several significant techniques have been introducedfor image processing and object detection owing to this advancement. The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several remarkable breakthroughs were achieved. This paper comprehensively reviews the data classification, history as well as architecture of CNN and well-known techniques bytheir boons and absurdities. Finally, a discussion for implementation of CNN over object detection for effectual results based on their critical analysis and performances is presented

Face detection and analysis systems has been growing in last few years for various applications. Since the hardware performance increase in last few years, useof Deep Learning, Convolution Neural Network, Face detection, Face analysis techniques is increasing and day by day developed models are breaking accuracies of previous models and research in various tasks. Facial analysis system with age, gender and emotion recognition have been proposed with good accuracies for real-time and non-real time both. The present research paper focuses to provide a robust system architecture for age, gender and emotion recognition in real time which can be use in commercial, healthcare, and many more industries. To achieve this a literature survey is done on the same topic with previous researches to compare their results. The final model architecture proposed in this research paper is efficient and fast and provides accurate results as compare to previous researches

Time series is a very common class of data sets. Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research topic nowadays. Anomaly identification covers intrusion detection, detection of theft, mistake detection, machine health monitoring, network sensor event detection or habitat disturbance. It is also used for removing suspicious data from the data set before production. This review aims to provide a detailed and organized overview of the Anomaly detection investigation. In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series

Music makes up a huge portion of the contents stored and used over the internet, with several sites and applications developed solely to provide music-related services to their users/ customers.Some of the most challenging tasks in this scenario would include music classification based on languages and genres, playlist suggestions based on music history, song suggestions based on playlist contents, top genres / songs based on listeners' rating, likes, number of streams, song loops, popularity of artists based on number of songs released per year, hit songs per year, etc. One of the most important stages to solve the above-mentioned challenges would be music genre classification. It would be impractical to analyze each and every song in a given database to identify and classify music genres, even though human beings are better at performing such tasks. Hence, useful Machine Learning algorithms and Deep Learning approaches may be used for accomplishing such tasks with ease. A thorough analysis to understand the different uses of Machine Learning and Deep Learning algorithms and relevance of such algorithms with respect to situations would be made to highlight and contrast the advantages and disadvantages of each approach. The outcomes of the optimized models would be visualized and comparedto the expected outcomes for better perception.

As people, eyesight is one of the most significant and depended upon in acquainting ourselves with where we are and what is around us. Freedom in exploring our new environment has to do with our capacity to survey these new environments, distinguish impediments as well as distinguish between objects in these environments. This task as simple as it may seem is one of the many problems encountered by the blind in their daily lives. Which hinders their ability to freely move about and carry out their day-to-day activities. Many types of research have been carried out to develop systems to assist the blind and the deaf. Some of these researches were developed with microcomputers as their central component. This study seeks to investigate the literature for studies that utilized raspberry pi to develop systems to assist the blind and the deaf. It as well attempts to define future research perspectives on the subject by addressing the research questions stated in this study. The finding of this study would enlighten researchers to be guided for future researches, as well as enable them to see the flaws in the area of study in other to solve such problems.