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Authors: Singh, Manwinder | Kumar, Manoj | Malhotra, Jyoteesh

Article Type: Research Article

Abstract: With the advancements in cognitive radio network procedures, such as blind sensing, sensing transmission trade-off, energy aware protocols etc, effectively overcomes the power constraint issues of CBAN. In present work, such primary issues have been addressed with the help of look-up table incorporated with energy harvesting (EH). It delivers maximum achievable throughput with minimum energy consumption to obtain self-sustainable pervasive wireless networks. The experimental work has been carried out in three different scenarios (Echelon 1, 2 and 3). The key components such as look-up table and energy harvesting have also been investigated on different system parameters (like energy consumption, normalized throughput, …saved and residual energy). The simulation is done with NS-2 and results are shown in Matlab for clarity. Results depict that proposed model consumes less energy and also provides a better normalised achievable throughput as compared to the conventional model. Show more

Keywords: Cognitive radios body area network (CBAN), look-up table (LUT), energy harvesting, ultra-wide band (UWB), cooperative sensing

DOI: 10.3233/JIFS-169669

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1253-1265, 2018

Authors: Chhabra, Megha | Sharan, Bhagwati | Kumar, Manoj

Article Type: Research Article

Abstract:  The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific …needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change. Show more

Keywords: User experience, fuzzy cognitive maps, modelling, quality experience, mobile applications

DOI: 10.3233/JIFS-222111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2957-2979, 2023

Authors: Rana, Anurag | Vaidya, Pankaj | Kautish, Sandeep | Kumar, Manoj | Khaitan, Supriya

Article Type: Research Article

Abstract: Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn …like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0 ), the corner frequency at which spectrum decays with a rate of 2(fc ), the cut-off frequency at which spectrum again decays (fmax ), and the rate of decay above fmax on its own (N ). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN. Show more

Keywords: Artificial neural networks, brune based application, adaptive neuro fuzzy inference system, source dimension, earthquake occurrence, prediction

DOI: 10.3233/JIFS-224423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3485-3500, 2023

Authors: Kumar, Manoj | Sharma, Sukhwinder | Mittal, Puneet | Singh, Harmandeep | Singh, Sukhwinder

Article Type: Research Article

Abstract: The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable …nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications. Show more

Keywords: IoT, Wireless Sensor Networks, load balancing, fuzzy logic, network lifetime

DOI: 10.3233/JIFS-234075

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Gautam, Devendra | Dixit, Anurag | Banda, Latha | Goyal, S.B. | Verma, Chaman | Kumar, Manoj

Article Type: Research Article

Abstract: In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. …In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. Show more

Keywords: Recommender system, confidentiality, deep learning, convolutional neural networks, fuzzy logics

DOI: 10.3233/JIFS-224257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5509-5522, 2023

Authors: Sran, Sukhwinder Singh | Singh, Harmandeep | Mittal, Puneet | Kumar, Manoj | Sharma, Sukhwinder

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234043

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024