Computational and Mathematical Methods in Medicine

Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.

Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

https://www.mdpi.com/1999-5903/11/4/94/pdf

A Review of Machine Learning and IoT in Smart Transportation

Fuzzy set theory, rough set theory and soft set theory are all generic mathematical tools for dealing with uncertainties. There has been some progress concerning practical applications of these theories, especially, the use of these theories in decision making problems. In the present article, we review some decision making methods based on (fuzzy) soft sets, rough soft sets and soft rough sets. In particular, we provide several novel algorithms in decision making problems by combining these kinds of hybrid models. It may be served as a foundation for developing more complicated soft set models in decision making.

A survey of decision making methods based on certain hybrid soft set models

A huge repository of terabytes of data is generated each day from modern information systems and digital technolo-gies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues.

/pdf/a-survey-on-big-data-analytics-challenges-open-research-1uqa26i8p7.pdf

A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

The rapid growth of Internet users led to unwanted cyber issues, including cyberbullying, hate speech, and many more. This article deals with the problems of hate speech on Twitter. Hate speech appears to be an inflammatory kind of interaction process that uses misconceptions to express a hate ideology. The hate speech focuses on various protected aspects, including gender, religion, race, and disability. Owing to hate speech, sometimes unwanted crimes are going to happen as someone or a group of people get disheartened. Hence, it is essential to monitor user’s posts and filter the hate speech related post before it is spread. However, Twitter receives more than six hundred tweets per second and about 500 million tweets per day. Manually filtering any information from such a huge incoming traffic is almost impossible. Concerning to this aspect, an automated system is developed using the Deep Convolutional Neural Network (DCNN). The proposed DCNN model utilises the tweet text with GloVe embedding vector to capture the tweets’ semantics with the help of convolution operation and achieved the precision, recall and F1-score value as 0.97, 0.88, 0.92 respectively for the best case and outperformed the existing models.

/pdf/a-framework-for-hate-speech-detection-using-deep-3irdjzxtc4.pdf

A Framework for Hate Speech Detection Using Deep Convolutional Neural Network

In this paper we have explained the detailed work done in developing a system which can be used for the purpose of opinion analysis of a product or a service. The system readily processes the tweets by pulling data from tweeter posts, preprocessing it and connecting to Alchemy API by REST call method. and showing the result graphically. We have given the analysis for the product Samsung Galaxy Our proposed system access the public tweets by API and filters them for Samsung Galaxy. The analysis is being carried out as to classify the sentiment as positive, negative or neutral.

Opinion mining about a product by analyzing public tweets in Twitter

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization’s profit as well as users’ satisfaction. A customer’s request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

/pdf/a-joint-resource-allocation-security-with-efficient-task-1a526lnj.pdf

A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques

A smart road lighting framework conforms to light yield in the view of use and inhabitance, i.e., computerizing grouping of pedestrians, cyclist, cars and public transports. The smart road light administration proposes the establishment of the remote based framework to remotely track and control the genuine vitality utilization of the road lights and take suitable vitality utilization decrease measures through power molding and control. The road light controller ought to be introduced on the shaft lights which comprise of microcontroller alongside different sensor and remote module. The road light controller introduced into the city light shaft will control LED road lighting relying upon the movement stream, convey information between every road light.

Smart lighting: Intelligent and weather adaptive lighting in street lights using IOT

Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data [9]. In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naive Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set.

Tapan Kumar Das

Papers

A Framework for Hate Speech Detection Using Deep Convolutional Neural Network

Opinion mining about a product by analyzing public tweets in Twitter

A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques

Smart lighting: Intelligent and weather adaptive lighting in street lights using IOT

A customer classification prediction model based on machine learning techniques