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Top 10 Cited research articles in the field of Soft Computing, Artificial Intelligence and Applications @ 2024

Big Data Analytics: Challenges And Applications For Text, Audio, Video, And Social Media Data

    Jai Prakash Verma, Smita Agrawal, Bankim Patel and Atul Patel,CSE Department, Institute of Technology, Nirma University, Ahmedabad,

    ABSTRACT

    All types of machine automated systems are generating large amount of data in different forms like statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we are discussing issues, challenges, and application of these types of Big Data with the consideration of big data dimensions. Here we are discussing social media data analytics, content based analytics, text data analytics, audio, and video data analytics their issues and expected application areas. It will motivate researchers to address these issues of storage, management, and retrieval of data known as Big Data. As well as the usages of Big Data analytics in India is also highlighted.

    KEYWORDS

    Big Data, Big Data Analytics, Social Media Analytics, Content Based Analytics, Text Analytics, Audio Analytics, Video Analytics.


    For More Details :
    https://aircconline.com/ijscai/V5N1/5116ijscai05.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2016.html




Bacteria Identification From Microscopic Morphology: A Survey

    Noor Amaleena Mohamad, Noorain Awang Jusoh, Zaw Zaw Htike and Shoon Lei Win,Department of Mechatronics Engineering, IIUM, Kuala Lumpur, Malaysia

    ABSTRACT

    Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria identification systems and recommends future direction of automatic bacteria identification.

    KEYWORDS

    Bacteria Identification, Cocci, Bacilli, Vibrio, Naïve Bayes, Machine Learning.


    For More Details :
    https://airccse.org/journal/ijscai/papers/3214ijscai01.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2014.html




Routing Wireless Sensor Networks Based on Soft Computing Paradigms: Survey

    Marwa Sharawi, Imane Aly Saroit, Hesham El-Mahdy, Eid Emary,Faculty of Computer Studies, Arab Open University, Egypt

    ABSTRACT

    Wireless Sensor Networks (WSNs) are defined as dynamic, self-deployed, highly constrained structured network. It`s high computational environment with limited and controlled transmission range, processing, as well as limited energy sources. The sever power constraints strongly affect the existence of active nodes and hence the network lifetime. In order to prolong the network life time we have to overcome the scarcity in energy resources and preserve the processing of the sensor nodes as long as possible. Power management approaches efficiently reduce the sensor nodes energy consumption individually in each sensor node and the adaptive efficient routing technique has greatly appeals a great attention in research. The potential paradigms of softcomputing (SC) highly addressed their adaptability and compatibility to overwhelm the complex challenges in WSNs. This paper is introducing and surveying some of the Soft Computing proposed routing models for WSNs that optimally prolongs its life time.

    KEYWORDS

    Wireless Sensor Network, Routing, Soft Computing.


    For More Details :
    https://airccse.org/journal/ijscai/papers/2413ijscai03.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2013.html




Fuzzy Logic Based Direct Torque Control of Induction Motor With Space Vector Modulation

    Fatih Korkmaz, İsmail Topaloğlu and Hayati Mamur,Department of Electrical and Electronics Engineering Çankırı Karatekin University, 18200,Çankırı, TURKEY

    ABSTRACT

    The induction motors have wide range of applications for due to its well-known advantages like brushless structures, low costs and robust performances. Over the past years, many kind of control methods are proposed for the induction motors and direct torque control has gained huge importance inside of them due to fast dynamic torque responses and simple control structures. However, the direct torque control method has still some handicaps against the other control methods and most of the important of these handicaps is high torque ripple. This paper suggests a new approach, Fuzzy logic based space vector modulation, on the direct torque controlled induction motors and aim of the approach is to overcome high torque ripple disadvantages of conventional direct torque control. In order to test and compare the proposed direct torque control method with conventional direct torque control method simulations, in Matlab/Simulink, have been carried out in different working conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional direct torque control method.

    KEYWORDS

    Direct torque control, Fuzzy logic control, Space vector modulation, Induction motor control..


    For More Details :
    https://airccse.org/journal/ijscai/papers/2613ijscai03.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2013.html




A Study on Graph Storage Database of NOSQL

    Smita Agrawal and Atul Patel, 1CSE Department, Institute of Technology, Nirma University, Ahmedabad, 2Dean of CMPICA, CHARUSAT University, Changa

    ABSTRACT

    Big Data is used to store huge volume of both structured and unstructured data which is so large and is hard to process using current / traditional database tools and software technologies. The goal of Big Data Storage Management is to ensure a high level of data quality and availability for business intellect and big data analytics applications. Graph database which is not most popular NoSQL database compare to relational database yet but it is a most powerful NoSQL database which can handle large volume of data in very efficient way. It is very difficult to manage large volume of data using traditional technology. Data retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are available. This paper describe what is big data storage management, dimensions of big data, types of data, what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic structure of graph database, advantages, disadvantages and application area and comparison of various graph database.

    KEYWORDS

    Big Data, Graph Database, NoSQL, Neo4j, graph


    For More Details :
    https://aircconline.com/ijscai/V5N1/5116ijscai04.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2016.html




Pattern Detection with Rare Item-Set Mining

    Mehdi Adda1 Lei Wu2 Sharon White2 Yi Feng 3, Computer Science, University of Quebec at Rimouski, Rimouski, Canada

    ABSTRACT

    The discovery of new and interesting patterns in large datasets, known as data mining, draws more and more interest as the quantities of available data are exploding. Data mining techniques may be applied to different domains and fields such as computer science, health sector, insurances, homeland security, banking and finance, etc. In this paper we are interested by the discovery of a specific category of patterns, known as rare and non-present patterns. We present a novel approach towards the discovery of non-present patterns using rare item-set mining.

    KEYWORDS

    Data mining, rare patterns, security, intrusion detection, item-set.


    For More Details :
    https://airccse.org/journal/ijscai/papers/0812ijscai01.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current.html




An SOM-based Automatic Facial Expression Recognition System

    Mu-Chun Su1 Chun-Kai Yang1Shih-Chieh Lin1 De-Yuan Huang1Yi-Zeng Hsieh1 andPa-Chun Wang2, Department of Computer Science InformationEngineering,National Central University,Taiwan

    ABSTRACT

    Recently, a number of applications of automatic facial expression recognition systems havesurfaced in many different research fields. The automatic facial expression recognition problem is a very challenging problem because it involves in three sub-problems: 1) face detection, 2) facial expression feature extraction, and 3) expression classification. This paper presents an automatic facial expression recognition system based on self-organizing feature maps, which provides an effective solution to the aforementioned three sub-problems. The performance of the proposed system was computed on twowell-known facial expression databases. The average correct recognition rates were over 90%.

    KEYWORDS

    Impulse Noise, Nonlinear filter, Adaptive Filters, Decision Based Filters..


    For More Details :
    https://airccse.org/journal/ijscai/papers/2413ijscai05.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2013.html




Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization Problems

    Imtiaz Korejo1 Shengxiang Yang2Kamran Brohi3 Z.U.A.Khuhro4 Institute of Mathematics & Computer Science, University of Sindh, Jamshoro,Pakistan

    ="text-align: center;font-size:20px;line-height: 22px;">Lisa Y. Chen and Wan-Ning Wu, I-Shou University, Taiwan

    ABSTRACT

    This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms. .

    KEYWORDS

    Multi-population approaches, adaptive mutation operator, multi-modal function optimization,genetic algorithms.


    For More Details :
    https://airccse.org/journal/ijscai/papers/0413scai01.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current2013.html




A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection

    Bipradas Bairagi1 Balaram Dey2Bijan Sarkar3 Subir Sanyal4 Department of Production Engineering, Haldia Institute of Technology, Haldia, INDIA

    ABSTRACT

    Selection of an industrial robot for a specific purpose is one of the most challenging problems in modern manufacturing atmosphere. The selection decisions become more multifaceted due to continuous incorporation of advanced features and facilities as the decision makers in the manufacturing environment are to asses a wide varieties of alternatives based on a set of conflicting criteria. To assist the selection procedure various Multiple Criteria Decision Making (MCDM) approaches are available. The present investigation endeavours to mitigate and unravel the robot selection dilemma employing the newly proposed Multiplicative Model of Multiple Criteria Analysis (MMMCA) approach. MMMCA is a novel model in which all performance ratings are converted into numerical values greater than and equal to unity and converting all non-benefit rating into benefit category. Each normalized weight is used as the index of corresponding normalized ratings those are multiplied to obtain the resultant score. The best alternative is associated with the highest resultant score. A real life example is cited in order to demonstrate and validate the applicability, potentiality, suitability, flexibility and validity of the proposed model. At last sensitivity analysis is carried out for making dynamic decision

    KEYWORDS

    Multiplicative Model of Multiple Criteria Analysis, Industrial Robot Selection, Sensitivity Analysis.


    For More Details :
    https://airccse.org/journal/ijscai/papers/1212scai01.pdf


    Volume Link :
    https://airccse.org/journal/ijscai/current.html








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