CSI Transactions on ICT 

About: CSI Transactions on ICT is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Cloud computing. It has an ISSN identifier of 2277-9078. Over the lifetime, 340 publications have been published receiving 1767 citations.

Digital forensic research: current state of the art

Abstract: Digital forensics is the process of employing scientific principles and processes to analyze electronically stored information and determine the sequence of events which led to a particular incident. In this digital age, it is important for researchers to become aware of the recent developments in this dynamic field and understand scope for the future. The past decade has witnessed significant technological advancements to aid during a digital investigation. Many methodologies, tools and techniques have found their way into the field designed on forensic principles. Digital forensics has also witnessed many innovative approaches that have been explored to acquire and analyze digital evidence from diverse sources. In this paper, we review the research literature since 2000 and categorize developments in the field into four major categories. In recent years the exponential growth of technological has also brought with it some serious challenges for digital forensic research which is elucidated. Within each category, research is sub-classified into conceptual and practical advancements. We highlight the observations made by previous researchers and summarize the research directions for the future.

A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques

Abstract: Data mining is an important research activity in the field of medical sciences since there is a requirement of efficient methodologies for analyzing and detecting diseases. Data mining applications are used for the management of healthcare, health information, patient care system, etc. It also plays a major role in analyzing survivability of a disease. Classification and clustering are the popular data mining techniques used to understand the various parameters of the health data set. In this research work, various classification models are used to classify thyroid disease based on the parameters like TSH, T4U and goiter. Several classification techniques like K-nearest neighbour, support vector machine and Naive Bayes are used. The experimental study has been conducted using Rapid miner tool and the results shows that the accuracy of K-nearest neighbour is better than Naive Bayes to detect thyroid disease.

Prediction of heart disease using data mining techniques

Abstract: The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.

Internet of Things: challenges and research opportunities

Abstract: Faster development of sensor and network technologies is facilitating immense deployment of Internet of Things (IoT) towards creating a smart world. In IoT, a massive number of heterogeneous resource–constraint devices communicate with each other without any human intervention and generate a huge amount of data. Unique research challenges posed by IoT are fascinating the research community. This paper presents some of the critical issues along with state of the art solutions towards them. In-depth discussion is provided on various key issues like heterogeneity and interoperability, scalability, QoS, and security. Directions for further researches in those areas are also pointed out.

Detection of spam reviews: a sentiment analysis approach

Abstract: Electronic shopping is highly influenced by online reviews posted by customers against the product quality. Some fraudulent pretenders consider this as an opportunity to write the spam reviews to upgrade or degrade product’s reputation. Hence, detection of those reviews are very essential for preserving the interests of users. To date, number of researches have been proposed in order to detect the spam reviews and to provide the genuine resources for customers and business person. However, we found few limitations in existing supervised approaches. First, most of the supervised approaches have used manual labelling of reviews into spam and non-spam. However, due to identical appearance of reviews manual labelling can not be considered as authentic. Second, the scarcity of spam reviews leads to data imbalance problem. Third, computing similarities among reviews naturally needs expensive computation. In this work, we propose a novel and robust, spam review detection system which efficiently employ following three features: (i) sentiments of review and its comments, (ii) content based factor, and (iii) rating deviation. To address the aforementioned limitations, we investigated all these features for only suspicious review list in which only those reviews have kept which received comments by peer users. The proposed system achieved the F $$_1$$ -score of 91%. The proposed system can be a great asset in spam detection system as it can be used as an stand-alone system to purify the product review datasets.