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

Authors: Hazarika, Ruhul Amin | Maji, Arnab Kumar | Sur, Samarendra Nath | Olariu, Iustin | Kandar, Debdatta

Article Type: Research Article

Abstract: Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different …stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%. Show more

Keywords: Alzheimer’s disease (AD), mild cognitive impairment (MCI), grey matter (GM), atrophy, skull stripping, magnetic resonance imaging (MRI), fuzzy membership function

DOI: 10.3233/JIFS-219279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1779-1792, 2022

Authors: Popa, Mihaela | Alexuta, Daniel | Balas, Valentina E.

Article Type: Research Article

Abstract: This paper is focusing on intelligent rooftop greenhouses. An initial mathematical model implying recirculation factors for greenhouse and underneath building ventilation systems was upgraded in the sense of reducing interactions among parameters, by discarding the recirculation factors. The initial approach relied on basic fuzzy-interpolative temperature controllers working with a network of ventilation fans, adapted to the changes of the weather conditions and of the building configuration by means of a central expert adaptive rule base. This paper proposes a flexible distributed fans network, locally adapted, working under the control of temperature self-adaptive interpolative controllers. This approach enables us to adapt …such buildings, that are now confined to warm temperatures, to a wide range of climates, to value a great part of renewable resources of our cities and to initiate a process of increasing the carbon offset at large scale. The new configuration is tested by simulations. Show more

Keywords: Urban agriculture, intelligent rooftop greenhouse, fuzzy-interpolative adaptive control

DOI: 10.3233/JIFS-219280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1793-1797, 2022

Authors: Roy, Sanjiban Sekhar | Goti, Vatsal | Sood, Aditya | Roy, Harsh | Gavrila, Tania | Floroian, Dan | Paraschiv, Nicolae | Mohammadi-Ivatloo, Behnam

Article Type: Research Article

Abstract: Fire calamity is one of the worst adversarial events that can happen to the human race. Fire disaster can happen as a manmade disaster or even naturally, and it may cause environmental, social, and financial damages as well. In order to minimalize the unwanted fire calamity, early detection of fire eruptions coupled with immediate and effective response is extremely vital to disaster management systems. The classification of forest fire and non fire images using deep learning techniques has recently received popularity. Detection and prevention of forest fire have lot of significance from the perspective of the forest fire department, specially …for the fire and arson investigators. There are shortcomings in the current mechanisms of forest fire detection in terms of accuracy. Hence, we propose a fire detection model using LeNet5 convolutional neural networks (CNN), which can spot fire in outdoor environments by classifying fire and non fire images. L2 regularization is critical technique that manipulates the complexity of the convolutional neural network model. In our work fire images have certain features that decide if the image is fire or non fire.A weight is assigned to every feature. Regularization used to help to reduce the over fitting that used to caused by plenty of weights. Our proposed provides the directiontowards developing a system that detects the early stages of forest fire.This model can further be utilized to prevent the damage caused by the fire. A CNN is a deep learning method, which has been adopted in order to detect the images of fire and non-fire. With the non sparse solution of L2 regularization we have obtained around 87% of train accuracy, 71% of validation accuracy and 70% of test accuracy after running 10 epochs. Show more

Keywords: Fire detection, convolutional neural networks, LeNet, deep learning, data augmentation

DOI: 10.3233/JIFS-219281

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1799-1810, 2022

Authors: Mng’ong’o, Benito G. | RAD, Dana | Koloseni, David | Balas, Valentina E.

Article Type: Research Article

Abstract: It is a common practice for organisation to carry out assessment exercises regarding performance of their organisational activities and processes. This paper assesses the market performance of five agro-processed crops at Sustainable Agriculture Tanzania (SAT) against some criteria. Experts at SAT supplied useful information by responding through a questionnaire. The fuzzy TOPSIS model was applied in the methodology to rank the processed products. For the sake of comparing results, the fuzzy analytical hierarchy process (AHP) model was also applied to rank the products. It was found that three products maintained their positions in the two models while the other two …products (alternatives) exchanged their positions. It was further suggested that more efforts have to go for lower market performing products by looking upon means to improve on their corresponding low weight criteria/sub-criteria. Show more

Keywords: Agro-processing, fuzzy TOPSIS, fuzzy AHP, linguistic variables, fuzzy aggregation, multi-criteria, decision making

DOI: 10.3233/JIFS-219282

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1811-1826, 2022

Authors: Roy, Sanjiban Sekhar | Mihalache, Sanda Florentina | Pricop, Emil | Rodrigues, Nishant

Article Type: Research Article

Abstract: In the recent time, enviromental sound classification has received much popularity. This area of research comes under domain of non-speech audio classification. In this work, we have proposed a dilated Convolutional Neural Network approch to classify urban sound. We have carried out feature extraction, data augmentation techniques to carry out our experimental strategy smoothly. We also found out the activation maps of each layers of dilated convolution neural network. An increamental dilation rate has exploited Overall we achieved 84.16% of accuracy from the proposed dilated convolutional method. The gradual increaments of dilation rate has exploited the worse effect of grindding …and has lowered down the computational cost. Also, overall classification performance, precision, recall,overall truth and kappa value have been obtained from our proposed method. We have considered 10 fold cross validation for the implementation of the dilated CNN model. Show more

Keywords: Convolutional neural network, classification, environmental sound classification, activation maps and accuracy

DOI: 10.3233/JIFS-219283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1827-1833, 2022

Authors: Khalifeh, Ala’ F. | AlQammaz, Abdullah Y. | Abualigah, Laith | Khasawneh, Ahmad M. | Darabkh, Khalid A.

Article Type: Research Article

Abstract: Weather prediction is paramount for many applications and scenarios, among them is agriculture. In order to efficiently irrigate the crops with the exact needed water amount, weather forecasting can be used to optimize the quantity of required irrigation water such that the crops are neither dried up nor over-irrigated. This paper proposes a Machine Learning (ML)-based weather forecasting model, which utilizes the Social Spider Algorithm-Least Square-Support Vector Machine (SSA-LS-SVM) algorithm. The simulation results are used to predict the prime weather and soil parameters such as the atmospheric temperature, pressure, and soil humidity for 24, 48, and 72 hours based on …previous 39 days’ hourly data for Amman city. The predicted values showed low relative mean square errors compared with the actual values and the LS-SVM predictor. Show more

Keywords: Weather forecasting, prediction, smart irrigation, artificial intelligence, social spider algorithm-least square support vector machine, least square support vector machine

DOI: 10.3233/JIFS-219284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1835-1842, 2022

Authors: Anh, Nguyen Thi Ngoc | Anh, Pham Ngoc Quang | Thu, Vu Hoai | Van Thai, Doan | Solanki, Vijender Kumar | Tuan, Dang Minh

Article Type: Research Article

Abstract: Anomaly detection for sensor systems is one of the most researched topics for the Internet of Thing systems. Researchers have been attracted to machine learning classification problems that are considered the most effective techniques. The novel model is proposed by combining anomaly pattern Symbolic Aggregate Approximation (SAX), processing imbalance data and machine learning techniques for sensor anomaly detection. The advantage of anomaly patterns and machine learning leads to the the proposed model to have better performance. The proposed model consists of three phases: finding anomaly pattern features, processing imbalanced data, exploring data by machine learning model. In this paper, the …main contributions with respect to previous works can be listed as follows: (i) Successful modeling the new method of SAX for time series data for finding complex and dynamic anomaly patterns. (ii) Archiving applied anomaly pattern feature into machine learning model Random Forest and hyperparameters optimisation of these model. (iii) Fitfully proposed a model combining SAX, imbalance technique, and random forest to anomaly detection. (iv) Achieving applied proposal model in automatic meter intelligence system in Vietnam. The experiential results of the proposed model have described the robustness and better performance for detecting anomalies of power meter sensors. Show more

Keywords: Time series, anomaly detection, intelligent meter, SAX, machine learning, pattern recognition

DOI: 10.3233/JIFS-219285

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1843-1852, 2022

Authors: Yarbakhsh, Reza | Mortazavi, Seyed Ali Reza | Mortazavi, SM Javad | Parsaei, Hossein | Rad, Dana

Article Type: Research Article

Abstract: The emergence of a new variant of SARS-CoV-2 in the UK that is spreading more rapidly has raised great concerns not only in the UK but also whole Europe and other parts of the globe. The newly identified variant of SARS-CoV-2 that is reported to be more contagious has prompted many countries to ban travel to and from the UK. As of April 2, 2021, nearly 4.35 million confirmed cases of coronavirus (COVID-19) have been reported in the UK out of which more than 127,000 people have died. These numbers reveal a need for predictor models to assist with management, …prevention, and treatment decisions. Here, we presented an Artificial Intelligence (AI) model to predict the death rate in various cities of the United Kingdom. Training and testing the model using the data available on the European data portal showed promising results with predicted R2  = 0.88. Show more

Keywords: COVID-19, artificial intelligence, death rate, prediction, UK

DOI: 10.3233/JIFS-219286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1853-1857, 2022

Authors: Vallent, Thokozani Felix | Hanyurwimfura, Damien | Kim, Hyunsung | Mikeka, Chomora

Article Type: Research Article

Abstract:  The modern grid has various functionalities by using remote sensor automation in power management, monitoring and controlling the system. Thus, it is imperative to ensure secure communications for various agents in smart grid, since the system is information communication based. Being information based the smart grid encounters security and privacy challenges impeding its adoption. One way of dealing with these cyber concerns is in devising robust cryptosystem for data encryption and authenticated key agreement in the communications of these remotely controlled smart devices. However, many proposed solutions are provided at the expense of computations cost. Thus, this paper designs a …novel authenticated key agreement scheme with anonymity based on widely acceptable elliptic curve cryptography with efficiency. The scheme ensures optimal computation and communication overload whilst achieving mutual authentication and anonymity in the key agreement process. The scheme is proven in both formal and informal security analysis in portraying its satisfaction of the standard and extended Canetti–Krawczyk (eCK) security requirements. A comparative analysis with related schemes indicates that the proposed scheme have merits over others. Show more

Keywords: Smart grid, authenticated key agreement, identity-based key agreement, integrity, non-repudiation, mutual authentication

DOI: 10.3233/JIFS-219287

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1859-1869, 2022

Authors: Tran, Thien Khai | Ta, Chien D.C. | Phan, Tuoi Thi

Article Type: Research Article

Abstract: Semantic relations have been adopted in many research fields, including the semantic web, information retrieval, and Q&A systems. The aim of the semantic relations is to remove conceptual and terminological confusion. This is achieved by specifying a set of general concepts that characterize domains and their definitions and interrelationships. This research describes how to detect semantic relations, including synonyms, hyponyms, and hypernym s based on WordNet and entities of a knowledge graph (KG). This KG was built from two resources: ACM Digital Library and Wikipedia. We used natural language processing and the deep learning approach for processing data before generating …the KG with an effective algorithm. We chose five of 245 categories in the ACM Digital Library to evaluate the proposed method. The generated results show that our system has excellent performance. Show more

Keywords: Semantic relations, knowledge graph, information extraction

DOI: 10.3233/JIFS-219288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1871-1876, 2022