Journal of Intelligent & Fuzzy Systems - Volume 43, issue 2

Authors: Yegnanarayanan, V. | Krithicaa Narayanaa, Y. | Anitha, M. | Ciurea, Rujita | Marceanu, Luigi Geo

Article Type: Research Article

Abstract: Cancer is a major research area in the medical field. Precise assessment of non-similar cancer types holds great significance in according to better treatment and reducing the risk of destructiveness in patients’ health. Cancer comprises a ambient that differs in response to therapy, signaling mechanisms, cytology and physiology. Netting theory and graph theory jointly gives a viable way to probe the proteomic specific data of cancer types such as ovarian, colon, breast, oral, cervical, prostate, and lung. We observe that the P2P(protein-protein) interaction Nettings of the cancerous tissues blended with the seven cancers and normal have same structural attributes. But …some of these point to desultory changes from the disease Nettings to normal implying the variation in the dealings and bring out the redoing in the complicacy of various cancers. The Netting-based approach has a pertinent role in precision oncology. Cancer can be better dealt with through mutated pathways or Nettings in preference to individual mutations and that the utility value of repositioned drugs can be understood from disease modules in molecular Nettings. In this paper, we demonstrate how the graph theory and neural Nettings act as vital tools for understanding cancer and other types such as ovarian cancer at the zeroth level. Show more

Keywords: Cancer, ovarian cancer, graph parameters, protein nettings, fuzzy neural nettings

DOI: 10.3233/JIFS-219289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1877-1886, 2022

Authors: Gupta, Punit | Saini, Dinesh Kumar | Rawat, Pradeep Singh | Bhagat, Sajit

Article Type: Research Article

Abstract: The service-oriented computing paradigm changes the way of computing. Emerging technologies like grid computing, cloud computing, and smart health care application have changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside the cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. E-Health care services …are one of the most computational intensive services in the cloud, they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This manuscript focuses on the task scheduling technique which enhances the performance in real-time with the least execution time, network cost, and execution cost. The presented model is inspired by Big Bang-Big Crunch algorithm in astronomy. The presented algorithm enhances the quality of service by reducing the scheduling delay, network delay with the least resource cost to complete the task in the least cost to the user with high quality of service. Show more

Keywords: BB-BC, cloud infrastructure, genetic algorithm, metaheuristic, task scheduling

DOI: 10.3233/JIFS-219290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1887-1895, 2022

Authors: Mysore, Aniruddha | TSB, Sudarshan

Article Type: Research Article

Abstract: Swarm Robotics is inspired by the biological swarms of social insects such as ants and bees, where individuals performing basic tasks give rise to complex behavior. It utilizes a team of cooperating robots to perform tasks more efficiently than possible by isolated robots. In this research, we study the exploration of unknown indoor areas using robots that coordinate with each other. In particular, we implement the Reverse Nearest Neighbor coordination algorithm with certain modifications to account for real-world constraints. The library developed as part of this work provides scripts to help with robotic tasks for exploration and robotic arm control …that can be used to set up simulation tools like VREP, without much prior experience thereby lowering the barrier for entry and making the robotics projects more accessible. Show more

Keywords: Multi-robot exploration, pedagogical robotics software

DOI: 10.3233/JIFS-219291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1897-1909, 2022

Authors: Asad, Muhammad Usman | Gu, Jason | Farooq, Umar | Balas, Marius | Chen, Zheng | Qureshi, Khurram Karim | Abbas, Ghulam | Chang, Chunqi

Article Type: Research Article

Abstract: This paper proposes a disturbance observer supported Takagi-Sugeno (TS) fuzzy model-based control scheme for uncertain systems. The baseline controller is a guaranteed performance fuzzy model based parallel distributed controller (PDC) which is constructed using the nominal system’s parameters. The model approximation error and parametric uncertainties are treated as a lumped disturbance and a nonlinear disturbance observer (NDOB) is introduced to counter the lumped disturbance. The applicability of the proposed scheme is demonstrated on the bilateral control of nonlinear teleoperation system in MATLAB/Simulink/QUARC environment through simulations as well as semi-real time experiments.

Keywords: TS fuzzy modeling, parallel distributed compensation, state convergence, teleoperation, MATLAB/Simulink/QUARC

DOI: 10.3233/JIFS-219292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1911-1919, 2022

Authors: Sabih, Muhammad | Umer, Muhammad | Farooq, Umar | Gu, Jason | Balas, Marius M. | Asad, Muhammad Usman | Qureshi, Khurram Karim | Khan, Irfan A. | Abbas, Ghulam

Article Type: Research Article

Abstract: This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters …are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments. Show more

Keywords: Engineering education, power system, equal area criterion, image processing, deep neural networks, MATLAB, pytorch

DOI: 10.3233/JIFS-219293

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1921-1932, 2022

Authors: Mundra, Shikha | Vijay, Shounak | Mundra, Ankit | Gupta, Punit | Goyal, Mayank Kumar | Kaur, Mandeep | Khaitan, Supriya | Rajpoot, Abha Kiran

Article Type: Research Article

Abstract: Thousands of patients around the world affecting their health with various factor as age, body mass index, cholesterol levels, albumin levels and several other factor. Prediction of health outcome due to these factors at a proper time can be served as an early warning. Recent growth in machine learning algorithm inspired us to build a predictive model for better healthcare facilities. In our work we have focused on problem of noisy and imbalanced dataset in which majority class is favored over minority one that leads to false prediction. We have experimented with two publicly available medical imbalanced dataset which varies …in its size as MIT’s GOSSIS death and PIMA Indians Diabetes Dataset based on binary class. In this model we have investigated 3 oversampling techniques (Synthetic Minority Oversampler, Random Oversampler and Adaptive Synthetic Sampler) along with two undersampling techniques (Random Undersampler and Near Miss) which were paired with 3 data reduction and cleaning methods namely Tomek Links, One Sided Selection and Edited Nearest Neighbors. At last, we found that combination of Adaptive Synthetic Sampler along with One Sided Selection perform better in case of large size dataset while combination of random oversampler along with Tomek Link showed better performance in case of low size data dataset. We have also analyzed that oversampling technique gives quite promising results in comparison to undersampling methods specifically when applied with machine learning classifiers as these classifiers are data hungry algorithms. Show more

Keywords: Synthetic Minority Oversampler (SMOTE), Random Oversampler (ROS), Adaptive Synthetic Sampler (ADASYN), Random Undersampler (RUS), near miss, Tomek Link (TL), One Sided Selection (OSS), Edited Nearest Neighbors (ENN)

DOI: 10.3233/JIFS-219294

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1933-1946, 2022

Authors: Gupta, Punit | Mundra, Shikha | Goyal, Mayank Kumar | Khaitan, Supriya | Dewan, Ritu | Mundra, Ankit | Rajpoot, Abha Kiran

Article Type: Research Article

Abstract: This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud …to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy. Show more

Keywords: ANN, BB-BC, resource optimization, fault

DOI: 10.3233/JIFS-219295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1947-1957, 2022

Authors: Gupta, Punit | Rawat, Pradeep | Tripathi, Rajan Prasad | Mundra, Ankit | Mundra, Shikha | Goyal, Mayank Kumar | Kaur, Mandeep | Agarwal, Ruchi

Article Type: Research Article

Abstract: Cloud computing in the current scenario comes with a large pool of resources, pay-per-use model and reliable infrastructure. Cloud optimization relies on resource optimization to improve the performance and reliability of the cloud. Fault in the cloud places an important role in defining the reliability of the cloud. The identification of fault is a challenging issue in a modular cloud environment. The researchers have developed various methods for the fault-aware scheduling of cloud resources. The fault-aware resource allocation includes static, dynamic, meta-heuristic, and learning-based approaches. In this article, we primarily focused on existing fault-aware resource allocation techniques and then we …proposed a model that will primarily focus on fault forecast in tasks allocation. The projected model is based nature-inspired heuristic approach and intelligent artificial neural network. The fault-tolerant aware ANN-based proposed model focuses on performance improvement and reliability testing proactively. The proposed model surpasses the existing state of art methods for proactive and reactive fault-aware scheduling techniques in a large scale datacenter. The results and discussions section support the reliability assertion of the fault-tolerant aware human brain and nature-inspired model. Show more

Keywords: ANN, Bat, cloud infrastructure, meta-heuristic, resource allocation

DOI: 10.3233/JIFS-219296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1959-1968, 2022

Authors: Sohail, Abid | Tariq, Muhammad Imran | Ali, Sehar | Butt, Muhammad Arif | Ismail, Muhammad | Ahmad, Farooq

Article Type: Research Article

Abstract: Diabetes is a complex disease that can only be controlled and prevented by a healthy lifestyle. We have selected the investigation of diabetes for this research as a substantially large fragment of our society is suffering from diabetes. It has been observed that diabetic patients are more expressive on social media as compared to real-life interactions. Furthermore, online communities are playing a significant role in providing social support and knowledge to patients through their experiences. Diabetes has only been monitored through wearable (sensor-based) and glucose meters. However, the problem arises when the patients become reluctant about giving the required information …themselves. For this purpose, a taxonomic system based on business process models has been developed which uses the textual data from the patients in which they express their emotions regarding Diabetes. Social media support groups related to Diabetes are used to gather data. Diabetic patients tend to share their emotions and feelings with people who are face a similar situation. However, there is no established measure to calculate the behavioral impact of diabetes on diabetic patients. In our research, we have studied how diabetic patients collaborate with each other to help others through social media and the impact of social communities on diabetic patient’s lifestyles. The results show the extent to which diabetic people follow a healthy lifestyle. Show more

Keywords: Diabetes, social media, business process modeling, abstraction, facebook, type 1 diabetic, type 2 diabetic

DOI: 10.3233/JIFS-219297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1969-1984, 2022

Authors: Naseer-u-Din, | Basit, Abdul | Ullah, Ihsan | Noor, Waheed | Ahmed, Atiq | Sheikh, Naveed

Article Type: Research Article

Abstract: Researchers used visual methods rigorously to improve brain tumor detection in MRI or CT scans, yet there remains a challenge to improve the detection accuracy. Further, the rise of deep learning methods improved tumor detection accuracy up to the mark. But again, many times, we face the challenges of having a bigger dataset and better computing power to achieve an improved and accurate trained model for every object classification problem. In this paper, we propose a deep learning framework single shot multi-box detector (SSD)-based model to detect tumors in the MRI scans. The proposed SSD model is the faster …algorithm to detect the tumor even with the ability to detect the smallest spot in the low-resolution MRI scans. We additionally used a lightweight neural network architecture MobileNet v2 with SSD for faster and accurate object classification. The experimental results showed 98% accuracy with the proposed method after training with the smallest dataset of 250 MRI scans. We used the Kaggle database for training and testing the proposed model. Show more

Keywords: Convolutional neural network (CNN), tumor detection, MobileNet model, segmentation, single shot detector (SSD), medical imaging

DOI: 10.3233/JIFS-219298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1985-1993, 2022