Pijush Kanti Ghosh

Bio: Pijush Kanti Ghosh is an academic researcher from Dr. B.C. Roy Engineering College, Durgapur. The author has contributed to research in topics: Agriculture & Fuzzy classification. The author has an hindex of 3, co-authored 3 publications receiving 308 citations.

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

Abstract: This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system

Abstract: Hypertension is called the silent killer because it has no symptoms and can cause serious trouble if left untreated for a long time. It has a major role for stroke, heart attacks, heart failure, aneurysms of the arteries, peripheral arterial diseases, chronic kidney disease etc. An intelligent and accurate diagnostic system is mandatory for better diagnosis and treatment of hypertension patients. This study develops a fuzzy expert system to diagnose the hypertension risk for different patients based on a set of symptoms and rules. Next we design a neuro fuzzy system for the same set of symptoms and rules using three different types of learning algorithms which are Levenberg-Marquardt (LM), Gradient Descent (GD) and Bayesian Resolution (BR) based learning functions. Then this paper presents a comparative study between fuzzy expert system (FES) and feed forward back propagation based neuro fuzzy system (NFS) for hypertension diagnosis. This paper also presents a comparison among the learning functions (LM, GD and BR) where Levenberg-Marquardt based learning function shows its efficiency over the others. Comparison between FES and NFS shows the effectiveness of using NFS over FES. Here, the input data set has been collected from 10 patients whose ages are between 20 and 40 years, both for male and female. The input parameters taken are age, body mass index (BMI), blood pressure (BP), and heart rate. The diagnosis process, linguistic variables and their values were modeled based on expert's knowledge and from existing database.

Ranking of Alternatives in Multiple Attribute Group Decision Making: A Fuzzy Preference Relation Based Approach

Abstract: This paper presents a two phase approach for fuzzy multiple attributes group decision making based on fuzzy preference relations (FPR). In the first phase, incomplete fuzzy preference relations provided by the decision makers are completed based on the procedure proposed by Herrera-Viedma et al. (2007). Second phase focuses the selection of best alternative(s) among a set of alternatives based on average rating value and score of alternatives with respect to decision makers. Initially the average rating value of each decision maker with respect to the alternatives are calculated and sorted in descending order. Based on their sorting, suitable score values are assigned to them and then calculate the summation values of the scores of the alternatives with respect to each decision maker. When summations of scores are same, summation of average rating value for each expert on individual alternatives is calculated to rank those alternatives. Larger summation values of the scores gives better choice to the alternative. Finally, this approach is analyzed with a numerical example and the result is compared with the experiment executed by Herrera-Viedma et al. (2007).

Social Impact of Primary Agricultural Co-operative Societies (PACS) in Nadia District of West Bengal

Abstract: Aims: The researcher has endeavored to analyze impacts of PACS in-terms of selected social parameters using perception of the sample respondents. The study is significant to evaluate the perception of members PACS. Place and Duration of Study: The researchers have employed 290 members in 29 PACS out of the 365 operating PACS found in the Nadia district of West Bengal. Primary data for the study have been collected during 2017-2019. Methodology: The researchers have employed a multi-stage random sampling technique for selecting 290 members in 29 PACS in the Nadia district of West Bengal. The Likert-scale used with 5 points in the questionnaire in which, the respondents were required to grade the scale of their satisfaction for particular thing. Data have been standardized for in the study with Zero mean and Unit Standard Deviation. Qualitative as well as quantitative techniques of data analysis were used Original Research Article Ghosh et al.; AJAEES, 39(12): 103-117, 2021; Article no.AJAEES.78274 104 to describe and analyze the research questions. The data collected from household survey were organized, coded and entered into statistical package, TANAGRA and Statistical Package of Social Sciences (SPSS). Descriptive statistics such as, frequency distribution, percentages etc. multivariate analyses for data reduction, Principal Component Analysis, K-means Cluster Analysis, analyses related to Group Characterization have been done to arrive meaningful interpretations for conclusions of the study. Results: The score obtained from the PCA are then grouped through cluster analysis. Social perception is to arrange the score according to deviations from Standard Deviation (SD). More than 76 percent of the total members fall in the group implying the perception of the members on social development by PACS is mostly homogeneous in nature. The researcher has find two variables namely, PACS role on empowering women in decision making and PACS Social business with other rural institutions comprise the first factor. Similarly second factor consist of the two (2) variables namely, PACS role in sensitizing women leadership in PACS management and role in skill development of women though training/ handholding etc. The second factor may be viewed as the factor of woman empowerment. Conclusion: The study concludes that PACS play important role in social development of the family. Most of the members agreed upon the positive role of the PACS Empowering women in decision making, Generating awareness of ongoing social development schemes of Government, Mobilizing of weaker sections, Educating Co-operative principles and Social business with other rural institutions. Moreover, PACS help to improve education level and improve habit of agricultural loan at the time of cash requirement particularly during peak season of agricultural operations.

Economic Impact of Primary Agricultural Cooperative Societies (PACS): Findings from Nadia District of West Bengal

Abstract: "With the passage of time and needs of rural people, the number and activities of PACSs have increased manifold and undergone changes with government patronage. This paper seeks to address how and to what extend the farming people have been benefited from PACS for their economic frontier in Nadia district of West Bengal. Primary data have been analysed through different statistical methods including multivariate analyses viz. principal component analysis, cluster analysis, and group characterization. The results show a positive relationship of the PACS’ role and family income of the farming community and majority of the sampled farmer members expressed the moderate role of PACS on overall economic development of farming community. Perception of the members on economic development by PACS is mostly homogeneous in nature. General perception of the members indicates the disappointing performances of the PACS in raising agricultural productivity or opening business opportunity at village level."

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Abstract: In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

Abstract: This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Abstract: This paper first reviews different methods of designing thermal error models, before concentrating on employing ANFIS models.The GM(1, N) model and fuzzy c-means clustering are used for variable selection, which is capable of simplifying the system prediction model.The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ?4µm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

Recent advances in neuro-fuzzy system: A survey

Abstract: Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific and engineering areas due to its effective learning and reasoning capabilities. The neuro-fuzzy systems combine the learning power of artificial neural networks and explicit knowledge representation of fuzzy inference systems. This paper proposes a review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017. The main purpose of this survey is to help readers have a general overview of the state-of-the-arts of neuro-fuzzy systems and easily refer suitable methods according to their research interests. Different neuro-fuzzy models are compared and a table is presented summarizing the different learning structures and learning criteria with their applications.