Diagnosis of Diabetes Using Bee Colony Algorithm and Fuzzy Decision Tree

Today, in medical knowledge, collecting a lot of data about different diseases is very important. Medical centers collect this data for various purposes. One of the goals of using this data is to research these data and obtain useful results and patterns in relation to diseases. The large volume of this data and the confusion used to overcome this problem to obtain useful relationships between risk factors in diseases. In this study, due to the importance of diabetes in medicine, the aim is to present a hybrid model using fuzzy decision tree and bee cloning algorithm to increase the accuracy of diagnosis. The proposed method is called ABC-FDT. In ABC-FDT, the number of optimal fuzzy sets for each feature is considered so that the best segmentation for the features is provided with the two goals of accuracy and reduction of complexity. PID diabetes data set and classification methods based on ID3, C4.5 and CART rules were used for evaluation. The results indicate the superiority of ABC-FDT algorithm in terms of rules of number, accuracy, sensitivity and specificity. ABC-FDT also outperformed the recently introduced GAANN, QFAM-GA, and SM-RuleMiner algorithms by 10.1, 5.4, and 7.8 percent, respectively

Multi-Level and Mutual Log Integrity Preservation Approach for Cloud Forensics using Public Key Infrastructure

The increasing growth of cloud computing makes the forensic investigation complex. It is very significant to collect and preserve the admissible evidences of different actions happened in the cloud environment in spite of collusion problem. The forensic investigation of logs poses a great challenge to make sure that the investigated logs are not tampered and consistent. Various integrity preservation methods are developed to secure the trustworthy evidences of cloud, but storing the logs of user actions and to ensure the trustworthiness with respect to forensic investigation still result a challenging issue in cloud. Hence, an effective multi-level and mutual log integrity preservation method is developed to assure the integrity of log evidence using public key infrastructure. The integrity preservation mechanism is modelled by employing the security mechanisms, such as hashing, encryption, decryption, and walsh transform. The communication between the entities is achieved by generating the keys using the hashing and encryption function with the concatenation operator. However, the public key and the random number is used to generate the message that can be exchanged between the cloud entities. The investigation process is done by the investigator for transferring the encrypted log file to the server. The proposed method achieved higher performance by considering the 500 number of cloud users in terms of detection rate, memory, and time with the values of 0.9828, 2185 bytes, and 7287 ms, respectively. &nbsp

A Secure Light Weight Authentication Protocol for Wireless Sensor Network in Internet of Things

With the advancement of cloud and Internet of Things (IoT) technology, mobile phones, RFID systems and wireless sensor networks can be integrated to form heterogeneous systems to execute smarter applications. However, data exchange between remote cloud and sensor node via internet poses critical security challenges. The major challenge is the authentication and key exchange among the communication agents. In addition, resource constrained devices such as RFID tags, sensors in WSN and IoT integration (WSNIT) would require robust and light weight authentication schemes. To combat these issues, we establish in this paper a first of its kind of a WSN security protocol in IoT, which is light weight and resistant to cryptographic attacks. &nbsp

An efficient and secure ID based Asymmetric Key Management Scheme for Dynamic Groups

A Group Key Agreement (GKA) protocol in Mobile Adhoc Networks (MANETS)permits a group of users to share a common secret using wireless links. Due to the advancement of technology, GKA poses a major challenge to provide authentication & confidentiality in open networks. This paper provides the diversified details of distributed group key management protocols from centralized group key management protocols and traditional symmetric group key agreement from asymmetric group key agreement protocols. In asymmetric group key management protocols, only a group encryption key is negotiated, and decryption key is kept secret at each user. In our proposed ID-based Asymmetric distributed group key agreement protocol, Chinese Remainder Theorem is used for computing Group Encryption Key and RSA algorithm is used for generating public and private keys for each member. The main advantage of our protocol is that it decreases the computation overhead of the members with respect to group dynamics and provides secure multicast communication in MANETs in comparison with other existing schemes. &nbsp

Optimization of Sentiment Analysis Methods via Machine Learning Algorithms

This research work focus on the latest studies that have used Machine learning to find a solution of sentiment analysis problems related to sentiment polarization. In preprocessing steps, the Models applied stop words and Bag of Words to a collection of datasets. Even with the widespread usage and acceptance of some approaches, a superior technique for categorizing the polarization of a text documents is tough to make out. Machine learning has lately evoked the attention as a method for sentiment investigation. The present work proposes a machine learning based hybrid algorithm that incorporate N-gram technique as a feature extraction and combines Decision tree classifier and Random forest Classifier techniques as a classification for sentiment analysis. Naïve bayse, linear classifier and support vector machine approaches are perform in the perspective of sentiment classification. Finally, a comparative study with the different supervised algorithm is implemented on product reviews dataset. The performance of models are evaluated on confusion matrix. In the comparative analysis of classification techniques, the combined technique has shown better results than previously used supervised techniques of naïve bayse ,linear classifier and support vector machine. &nbsp

An Audit of Indian Clinical Laboratories’ websites: Analysis of Optimization Suitability for Search Engines and Digital Marketing

Search engine optimization is an important aspect of the digital world we live in today. An extensive literature review showed only an occasional report of analysis of search engine optimization of medical services’ websites. We analyzed 16 websites of clinical laboratories in India for their search engine optimization status through features like secured socket layer, robots.txt and sitemap.xml file, page quality-related tags, page loading speed, link analysis, structured data, and “viewport” tag for mobile responsiveness. Few shortcomings were identified such as thin and duplicate page content in 50% of the websites, lack of structured data in 44.75%, and absence of robots.txt, sitemap.xml files and ‘alt’ and ‘srcset’ tags from 18.75%, 25%, 56.25% and 87.5% websites, respectively. Avoiding these short comings may improve organic traffic to the websites beneficial to both the marketer and the consumers. &nbsp

Provide a Dynamic Routing Algorithm for MPLS Networks Using Fuzzy Filtering Approach

The advent of Multiprotocol Label Switching (MPLS) technology is the basis of the next generation network to improve multimedia applications. One of the most basic traffic engineering concepts in MPLS is routing for Layered Service Provider (LSP). The purpose of providing routing algorithms is to maximize the number of routed requests according to QoS satisfaction. Most research in this area focuses solely on bandwidth, and relatively few studies consider both bandwidth limits and delay. In this paper, a dynamic routing algorithm based on a fuzzy filtering method is proposed which considers both bandwidth limits and end to end delay for route search. The fuzzy system is a prediction model based on fuzzy weighting rules for filtering high-resource requests. The proposed method tries to postpone requests with high bandwidth and maximum end to end delay. Various scenarios have been used to simulate and evaluate the efficiency of the proposed method, and criteria such as the number of requests accepted, average path length and load balance have been measured. The simulation results prove that the proposed method provides optimal routing performance for MPLS networks. &nbsp

Providing a Web Recommender System Using Markov Chains and Registration Files Structure

Today, due to the increasing growth of the internet and the huge amount of information we need systems to be able to recommend the most appropriate services and products to the user. The systems that do this are recommender systems. These systems are intelligently using artificial intelligence techniques to identify the interests of your users on the internet and suggest tailored offers to the user’s preferences and interests. Today, Markov models commonly used to predict web pages. For this purpose, in this research, we use a new Markov model and use the structure of registration files to predict the next pages obtained by the user. The proposed Markov model is based on a matrix of 1 to k and in the form of a Markov model which predicts the next pages. In order to reduce the complexity of the search space, as well as to better navigate to the recommender system, we use k-means clustering to group users. The results of the evaluations of the proposed method on the NASA web server log file and in the F-Measure criterion shows 0.57% superiority over BCF. &nbsp

An Improvement of Minimum Variance Distortionless Response Filter

In this paper, the author introduces an improvement of Minimum Variance Distortionless Response’s performance, which uses a priori information of speech presence probability to estimate the necessary matrix of noise. The proposal algorithm computes the smoothing parameter, that adapts with the presence or absence of speech components. The significant amount of noise reduction has provided the effectiveness and ability of increasing the signal-to-noise ratio of this algorithm’s speech enhancement. Post-filtering is an additional technique to enhance the quality of the output signal. The evaluation is presented in promising results of amplitude, spectrogram of original and processed signals. &nbsp

Development of an India-Specific Cancer Web Portal: An Educational Tool for Cancer Awareness and Community Engagement

Dissemination of adequate information about cancer is a prerequisite for focused cancer control efforts. This study was aimed at assessing the content validity of the existing web-based cancer awareness resources and using these inputs to create an innovative India-specific website for cancer prevention. A gap analysis of 65 existing websites on various types of cancer was carried out. The websites were graded for their content validity on a scale of 1 (good for minimally educated individual) to 6 (for health professionals only) and for external validity for India on a scale of 1 (not valid) to 3 (good validity). The websites were analysed for their layout, user friendliness, ease of navigation and complexity of the language. None of the 65 websites provided comprehensive information on the prevalent cancers in India. Majority (60%) of the websites had a score of 3 in content validity while external validity scored at 2 for 78.5% websites. In the view of the identified gaps, a comprehensive India-specific cancer website ‘India Against Cancer” was developed to provide basic information on cancer to the public with an intent to promote cancer awareness. ‘India Against Cancer’ is the first comprehensive India-specific website which is user friendly and provides authentic peer-reviewed information on prevalent cancers in India in a simple language. Wider use of this educational tool can potentially assist in improving cancer awareness and provide impetus to the population-based cancer screening program currently in the process of being rolled out in the country. &nbsp