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

Authors: Soomro, Saima Siraj | Jalbani, Akhtar Hussain | Channa, Muhammad Ibrahim | Lakho, Shamshad | Memon, Imran Ali

Article Type: Research Article

Abstract: The World Health Organization has stated Covid-19 as a pandemic that has posture a current hazard to humanity. Covid-19 pandemic has magnificently forced global shutdown of several events, including educational activities. This has caused in tremendous crisis-response immigration of educational institutes with online smart learning helping as the educational platform. Smart learning targets at providing universal learning to students consuming modern technology to completely prepare them for a fast-changing world everywhere. In this research paper an evaluation system has been developed that is based on bloom taxonomy. A Neuro-fuzzy system for the training and testing of the data for smart …and traditional learning outcomes has been applied on collected data. For this research work, we have selected students of the computing discipline and focus on core-computing subjects. The findings of this research work shows the importance of smart learning and its positive impact on student learning outcomes. The evaluation criteria are based on revised bloom taxonomy levels, such that all six levels have been covered. The students’ performance are very much encouraging when compared with ground truth values and reported 91.2% overall accuracy of proposed model on collected samples. Show more

Keywords: Revised bloom taxonomy, ITS, smart learning, neuro-fuzzy designer

DOI: 10.3233/JIFS-219299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1995-2004, 2022

Authors: Leghari, Mehwish | Memon, Shahzad | Dhomeja, Lachhman Das | Jalbani, Akhtar Hussain | Chandio, Asghar Ali

Article Type: Research Article

Abstract: Handwritten signature for the identification and authentication of an individual has been widely used in the biometric systems. Due to the intra-class and inter-class variabilities, signature verification has become one of the most challenging problem in the biometric technology. Furthermore, the offline handwritten signature can be forged by the skilled persons due to its static nature. Therefore, in this paper a deep learning-based method using convolutional neural network (CNN) for online signature verification has been developed. Different values of the convolutional kernels such as 1×1, 3×3 and 5×5 are used to extract the discriminative features at multi-scales. The features of …the initial and middle layers of the CNN are combined to create more powerful features. An up-sampling method with bilinear interpolation has been used to add the features of convolutional layers with different spatial dimensions. Both the addition and concatenation methods have been used to aggregate the convolutional features. A convolutional transpose method is applied to increase the depth of the convolutional layers while performing an addition operation on the layers with different depths. Finally, the concatenated features are passed to the fully connected layers for high-level feature extraction and classification. To evaluate the performance of the proposed method, an android application was developed where; a custom database of 985 online signatures collected from 197 users has been created. The problem of inadequate training data for online signature verification has been addressed through the data augmentation method. The experimental results show that the deep aggregated convolutional feature representation method achieves an accuracy of 99.32% on the custom developed online signature database. Show more

Keywords: Online signature verification, biometric signature verification, convolutional feature aggregation, deep learning based signature authentication, feature concatenation, convolutional neural network

DOI: 10.3233/JIFS-219300

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2005-2013, 2022

Authors: Lakho, Shamshad | Jalbani, Akhtar Hussain | Memon, Imran Ali | Soomro, Saima Siraj | Chandio, Asghar Ali

Article Type: Research Article

Abstract: With the spread of the COVID-19 pandemic, the importance of online learning has grown up worldwide and many higher education institutions used this mode of learning to save the timings of students. Just Online learning does not fulfill all the learning requirements of undergraduate students, therefore, there is a need for the blended learning (BL) method to be adopted in higher educational institutes for the enhancement of students’ learning outcomes. This research paper focuses on the development of an integrated blended learning model and the performance of the model on students’ learning has been predicted using a Bayesian network (BN) …classifier. The proposed model is based on the medium impact blend of the Rotation model and the Enriched Virtual Model and applied to undergraduate computing students. The Data Structures and Algorithms course is targeted for the prediction of students’ performance. The findings of the proposed Integrated BL model show that when students properly attend the classroom lectures followed by their associated lab practical in the Rotation Model and follow the online learning activities in the Enriched Virtual Model properly, then their learning outcomes may be increased as predicted using BN method. The proposed model also reports an overall accuracy of 88.5% on the collected data. Show more

Keywords: Integrated blended learning model, Bayesian network, data structures & algorithms, student performance, intelligent tutoring system

DOI: 10.3233/JIFS-219301

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2015-2023, 2022

Authors: Narejo, Sanam | Shaikh, Anoud | Memon, Mehak Maqbool | Mahar, Kainat | Aleem, Zonera | Zardari, Bisharat

Article Type: Research Article

Abstract: Hundreds of people dying from heart disease almost every day that is how terrific a delayed diagnosis can be. Living in an advanced era full of intelligent systems, the increasing number of deaths can be reduced. This research paper focuses on the development of a cardiovascular disease prediction system particularly a heart disease, by developing machine learning classifiers, for instance, Support Vector Machine (SVM), Decision Tree, and XGBoost Classifiers. We also scaled the features to standardize unconstrained features in data, available in a fixed range for better optimization of models. For efficiency, the classification of features was also done in …two categories, Independent features, and dependent features. Furthermore, the performance measures helped with best practices for model assessment & classifier performance. Eventually, after tuning hyper-parameters, the results exhibit high accuracy for XGBoost among other trained classifiers. After a comparative analysis, the best-suited algorithm can be utilized for heart disease detection, in the medical field, and regarding the economy, as costly treatments are taken into consideration. This indicates that a non-expert can also attempt for diagnosis without fretting over expensive treatments. Show more

Keywords: Medical diagnosis, heart disease, exploratory data analysis, machine learning classification, support vector machines, decision tree, XGBoost classifiers

DOI: 10.3233/JIFS-219302

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2025-2033, 2022

Authors: Shaikh, Anoud | Mahoto, Naeem Ahmed | Unar, Mukhtiar Ali

Article Type: Research Article

Abstract: The shift in the news consumption from traditional newspapers to online news has led media analysts and researchers to apply powerful text mining techniques on the vast amount of news data. News has a profound influence on public as it informs about the events happening around them and may affect them. It keeps the people connected and allows them to engage in the decision making process. The words used in the news language are sometimes taken from the regional languages so as to express a new phenomenon, event or idea. In this paper, we have proposed a lexicon based framework …named as TextGraph that automatically extracts the concepts from the Dawn news using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting factor and visualizes them in a formal way. To achieve value-add insights, we have developed Pakistani English corpus and used it along with other existing dictionaries. Our proposed corpus incorporates the Pakistani English words used in the Dawn news stories, which are annotated and validated by a human expert. Experimental results show that our concept extraction method out performs and gives more specific concepts. Our research suggests that the proposed framework and corpus opens multiple directions for promising future research in this domain. Show more

Keywords: Lexicon, concept extraction, concept visualization, text analysis

DOI: 10.3233/JIFS-219303

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2035-2044, 2022

Authors: Sanjrani, Anwar Ali | Baber, Junaid | Bakhtyar, Maheen | Ullah, Ihsan | Naveed, M. Shumail | Noor, Waheed | Basit, Abdul | Khan, Azam | Sheikh, Naveed

Article Type: Research Article

Abstract: The accuracy on MINST dataset for roman numerals is already 99.65%. However, same models showed low accuracy on Sindhi numerals. It is because Sindhi numerals have high correlation between the shapes of the numerals. In this paper, correlation based template matching is used to analyze the shape ambiguity by identifying the dominant false positives (FP) and false negatives (FN) for every numeral. Furthermore, the Gradients Histogram Orientation (GOH) features are used to improve the accuracy of existing classifiers by image-to-image matching. The classical OCR using simple binary features are not sufficient to address the problems of shape ambiguity in Sindhi …numerals, i.e., the shape of digits 2, , and 3, , are very similar. The raw pixel values are used as features for the classification in the first stage. In second stage, the input image is matched with the dominant FP and FN of the predicted class, and the final decision is made by the image-to-image matching based on GOH features. Decision based on image to image matching with dominant FP and FN increase the accuracy of the classifier. Support vector machine (SVM), K-nearest neighbor, and template based matching classifiers are used. The proposed extension substantially improves the accuracy of all mentioned classifiers. Show more

Keywords: Gradient orientation histograms, SIFT, gradient based keypoint descriptors, keypoint descriptor quantization

DOI: 10.3233/JIFS-219304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2045-2056, 2022

Authors: Sabir, Imran | Baber, Junaid | Ahmed, Atiq | Sheikh, Naveed | Bakhtyar, Maheen | Khan, Azam | Devi, Varsha

Article Type: Research Article

Abstract: Electrocardiogram (ECG) data recorded by medical devices are hard to analyze manually. Therefore, it is important to analyze and categorize each heartbeat using machine learning. Recently, advancements in machine learning have made classification of complex data easy and fast. However, these machine learning algorithms require sufficient amount of training data and have limited performance in case the data is imbalance. In case of MIT-BIH arrhythmia dataset, the distribution of training instances are quite imbalance. Many machine learning, particularly deep learning, algorithms give high accuracy on these datasets but still the minority classes have zero accuracy. In this paper, we improve …the accuracy of minority classes without hurting the overall accuracy of other classes using transfer learning. The accuracy of existing deep learning model is increased from 90.67% to 98.47%, respectively. Show more

Keywords: Transfer learning, deep learning, imbalance dataset

DOI: 10.3233/JIFS-219305

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2057-2067, 2022

Authors: Butt, Sahrish | Bakhtyar, Maheen | Noor, Waheed | Baber, Junaid | Ullah, Ihsan | Ahmed, Atiq | Basit, Abdul | Kakar, M. Saeed H.

Article Type: Research Article

Abstract: Unstructured text processing is the first step for several applications such as question answering systems, information retrieval, and recipe classification. In the field of recipe classification, number of frameworks have been proposed. However, it is still very tedious and time consuming to extract the food items from the unstructured text and then process for classification. In this research, an automatic food item detection from unstructured text is proposed based on semantic sense modeling. The candidate nouns are detected which can be food items and then the similarity of those nouns is computed with possible food categories. The candidate noun …is treated as food item if the similarity is high. For similarity between possible food item and food category is computed by WordNet ontology. The proposed framework is evaluated on benchmark datasets and competitive performance have been achieved. The F-score on large dataset that contains around 20 K recipes is 0.89 which is improved from 0.56. Show more

Keywords: Food named entity recognition, recipe text processing, NLP, semantic similarity, WordNet

DOI: 10.3233/JIFS-219306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2069-2078, 2022

Authors: Ali, Basit | Tayyaba, Shahzadi | Ashraf, Muhammad Waseem | Tariq, Muhammad Imran | Imran, Muhammad | Akhlaq, Maham

Article Type: Research Article

Abstract: IoT systems base devices are considered an excellent research domain owning to its expertise and applications in wide range of areas. IoT in health care domain is gaining attention due to its better access to the doctor and paramedical staff as well as sensor based studies which results in less man to man interacting and less fault in the data. The health care provider can easily access the vitals and various other medical parameters by even staying miles away from the patient. However, large amount of data transfer over various communication mediums results in more data traffic. This data transfer …will require more power which will be utilized to transfer the data. To reduce this data traffic issues, an efficient method is used in this work in which only the data that is predominantly important to be send to the health care provider is send via the communication medium. Rule based fuzzy logic tool is used in this work for an elder patient having cardiac issues. Blood sugar (After eating), Blood pressues (systolic), Blood pressure (Diastolic) and cholesterol level are taken as the parameter that are examined for the patient and the medical treatment required is calculated. The rules are set on the basis of real time data and human knowledge. The results from the fuzzy logic interference shows that the health care provider will be alarmed using communication medium only when active or emergency medical treatment of the patient is required. A comparative study between the power utilized in normal data driven method and fuzzy method shows that the fuzzy method utilize 8 times less power than the normal method. The simulated and MAMDANI model calculated values shows less than 1% error which shows the accuracy of the work in health care domain. Show more

Keywords: Fuzzy logic, healthcare system, Internet of Things

DOI: 10.3233/JIFS-219307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2079-2085, 2022

Authors: Tariq, Nimra | Ashraf, Muhammad Waseem | Tayyaba, Shahzadi

Article Type: Research Article

Abstract: The uniformly smooth and sharp microneedles have great significance in contact spectroscopy, 3D printing, biomedical and nanotechnology. The stability, bio-stability, conductivity and mechanical properties of the gold (Au) make it effective rather than the other metals such as tungsten, copper, platinum and graphite. The surface quality, proper dimension such as the tip, cone angle is the matter of the trial and practice matter. It was the main issue to develop a controlled optimized methodology to obtain the gold needles of specific dimensions in regular and systematic way. The Ansys simulation of solid microneedle has been done to check on what …stress the deflection occurs on microneedles. Then fuzzy optimization has performed to optimize the parameter of the etching set up such as the voltage, current and time of etching as an input parameter and the tip size and the conical section length as the output parameters. After the simulation and optimization the experiment of the etching has performed with the 3M solution of NaCl in deionized water and small amount of hydropercaloric acid. The fabricated needles have been then characterized by Scanning electron microscopy (SEM) to observe the morphology and the dimensions. The fuzzy analysis has been performed for optimization of the inputs voltage of range 1–10volt, current of range 1–100 mA and etching times from 1–15minutes. These optimized values are calculated by the fuzzy analysis such as the voltage is 58.6 mA, etching time 15 minutes and the voltages found to be 10 volt. Fuzzy analysis gives the simulated size of the tip 10.6μm and Mamdani models gives the 10.7μm which have the 0.01% error and the cone length for the Mamdani was found to be 500μm and the simulated values 497 having the 0.03% error which have very close approximation with the experimental values from the SEM micrographs that which also gives the values of the cone length from 400–500μm and the tip size from 10-20μm for the time 10-15minute whose values was optimized by the fuzzy analysis. Show more

Keywords: Microneedles, ANSYS simulation, fuzzification

DOI: 10.3233/JIFS-219308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2087-2097, 2022

Authors: Manzoor, Saher | Tayyaba, Shahzadi | Ashraf, Muhammad Waseem

Article Type: Research Article

Abstract: Microfluidic filtration is an essential process in many biomedical applications. Micro or nanoporous membranes are used for colloidal retention. During the membrane filtration process visualization of various phenomena is challenging. Theoretical models have been proposed to visualize the transport mechanism. In this work, ANSYS Fluent is used for 3D designing of the microfluidic system and Fuzzy simulations are used to study flow rate and velocity, to get the maximum benefit from Anodized Aluminium oxide membrane in practical applications. The proposed method exploits relations between driving force, membrane area, and fluid flow. After optimization of parameters for the filtration, the AAO …membrane with desired pore diameter was fabricated using the two-step anodization method. Scanning electron microscope is used for characterization of fabricated AAO membrane. The simulated and theoretical results using computer-based programs are then compared for manipulation of flow rate during the filtration process. Along with the manipulation of flow rate from nanoporous membrane other challenges faced during the filtration process are also highlighted with possible solutions. Show more

Keywords: AAO, microfluidic analysis, fuzzy analysis, filtration

DOI: 10.3233/JIFS-219309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2099-2108, 2022

Authors: Imran, Muhammad | Butt, Alvina Rafiq | Qasim, Faheem | Fahad, Hafiz Muhammad | Sher, Falk | Waseem, Muhammad Faisal

Article Type: Research Article

Abstract: ZnO is promising material for the electronic and optoelectronic devices. In present work we have fabricated the ZnO film by DC reactive magnetron sputtering. The variations of reactive and sputtering gases affect the crystallite size and band gap of ZnO film. In present work the ZnO film is prepared at 50 watt power by DC reactive spurting method. The fuzzy simulation has been performed to estimate the best argon oxygen gas ratio which gives the better crystallinity and band-gap. The structural analysis shows that the ZnO film has hexagonal wurtzite structure. The UV-vis spectroscopy has been employed to find the …band gap.the measured band gap value of ZnO is 3.21 eV. The fuzzy rule based system and characterization results are in accordance with each other with a minimal difference of less than 1%. Show more

Keywords: DC Sputtering, Zinc oxide film, band gap, resistivity

DOI: 10.3233/JIFS-219310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2109-2114, 2022

Authors: Awan, Saima Ashraf | Tariq, Muhammad Imran | Zhuang, Peifen

Article Type: Research Article

Abstract: This research analyzed the relationship between openness to agricultural exchange, foreign direct investment, capital, consumer price index, and GDP in Pakistan utilizing time series data for the 1971-2019 period. We used autoregressive distributed lag ARDL-bound testing method to analyze the long-run and short-run correlation between projected variables. The study’s findings also confirmed a positive and important long-term correlation between openness to agricultural trade, foreign direct investment and Pakistan’s economic development. There is no long-run relationship between consumer price and economic growth.

Keywords: Agricultural trade openness, foreign direct investment, ARDL approach

DOI: 10.3233/JIFS-219311

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2115-2119, 2022

Authors: Ma, Yingran | Peng, Yanjun | Wu, Tsu-Yang

Article Type: Research Article

Abstract: Transfer learning technique is popularly employed for a lot of medical image classification tasks. Here based on convolutional neural network (CNN) and sparse coding process, we present a new deep transfer learning architecture for false positive reduction in lymph node detection task. We first convert the linear combination of the deep transferred features to the pre-trained filter banks. Next, a new point-wise filter based CNN branch is introduced to automatically integrate transfer features for the false and positive image classification purpose. To lower the scale of the proposed architecture, we bring sparse coding process to the fixed transferred convolution filter …banks. On this basis, a two-stage training strategy with grouped sparse connection is presented to train the model efficiently. The model validity is tested on lymph node dataset for false positive reduction and our approach indicates encouraging performances compared to prior approaches. Our method reaches sensitivities of 71% /85% at 3 FP/vol. and 82% /91% at 6 FP/vol. in abdomen and mediastinum respectively, which compare competitively to previous approaches. Show more

Keywords: Deep learning, lymph node false positive reduction, transfer learning method, sparse coding algorithm

DOI: 10.3233/JIFS-219312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2121-2133, 2022

Authors: Jia, Han-Dong | Li, Wei | Pan, Jeng-Shyang | Chai, Qing-Wei | Chu, Shu-Chuan

Article Type: Research Article

Abstract: Wireless sensor network (WSN) is a network composed of a group of wireless sensors with limited energy. With the proliferation of sensor nodes, organization and management of sensor nodes become a challenging task. In this paper, a new topology is proposed to solve the routing problem in wireless sensor networks. Firstly, the sensor nodes are layered to avoid the ring path between cluster heads. Then the nodes of each layer are clustered to facilitate the integration of information and reduce energy dissipation. Moreover, we propose efficient multiverse optimization to mitigate the impact of local optimal solution prematurely and the population …diversity declines prematurely. Extensive empirical studies on the CEC 2013 benchmark demonstrate the effectiveness of our new approach. The improved algorithm is further combined with the new topology to handle the routing problem in wireless sensor networks. The energy dissipation generated in routing is significantly lower than that of Multi-Verse Optimizer, Particle Swarm Optimization, and Parallel Particle Swarm Optimization in a wireless sensor network consisting of 5000 nodes. Show more

Keywords: Routing, clustering, layering, WSN, multi-verse optimizer

DOI: 10.3233/JIFS-219313

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2135-2146, 2022

Authors: Feng, Naidan | Wu, Tsu-Yang | Liang, Yongquan

Article Type: Research Article

Abstract: The electrocardiogram (ECG) signal is a kind of time-varying signal, which has the characteristics and difficulties of variability, instability, and noise. Aiming at that, this paper put forward a novel 13-layer deep dynamic neural network model (DDNN) for the ECG signal learning and classification. The proposed DDNN model is a dynamic hybrid deep learning model. It includes a wavelet block, a convolutional block, a recurrent block, and a classification block, which combines the learning property and classification mechanism of convolutional neural network for the large-scale data sets, the learning and memory ability of Long Short-Term Memory (LSTM) for time series, …and the noise reduction and processing ability of wavelet basis for the signals to meet the requirement of the learning and classification of ECG signal characteristics. Sufficient experimental results show that the proposed model is feasible and effective in the electrocardiogram signal pattern classification. Show more

Keywords: Dynamic signal classification, deep dynamic neural network, time-varying signal, ECG signal classification

DOI: 10.3233/JIFS-219314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2147-2154, 2022

Authors: Feng, Qing | Pan, Jeng-Shyang | Du, Zhi-Gang | Peng, Yan-jun | Chu, Shu-Chuan

Article Type: Research Article

Abstract: Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration …and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data. Show more

Keywords: ALO, quantum-behaved, opposite learning, gaussian mutation, feature selection

DOI: 10.3233/JIFS-219315

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2155-2166, 2022

Authors: Wu, Jimmy Ming-Tai | Tsai, Meng-Hsiun | Li, Tu-Wei | Pirouz, Matin

Article Type: Research Article

Abstract: Estimating similarity using multiple similarity measures or machine learning prediction models is a popular solution to the link prediction problem. The Relation Pattern Deep Learning Classification (RPDLC) technique is proposed in this study, and it is based on multiple neighbor-based similarity metrics and convolution neural networks. The RPDLC first calculates the characteristics for a pair of nodes using neighbor-based metrics and impact nodes. Second, the RPDLC creates a heat map using node characteristics to assess the similarity of the nodes’ connection patterns. Third, the RPDLC uses convolution neural network architecture to build a prediction model for missing relationship prediction. On …three separate social network datasets, this method is compared to other state-of-the-art algorithms. On all three datasets, the suggested method achieves the greatest AUC, hovering around 99 percent. The use of convolution neural networks and features via relational patterns to create a prediction model are the paper’s primary contributions. Show more

Keywords: Link prediction problem, convolution neural network, relation pattern, social network

DOI: 10.3233/JIFS-219316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2167-2178, 2022

Authors: Wu, Jimmy Ming-Tai | Tsai, Meng-Hsiun | Cheng, Chao-Chieh | Wu, Mu-En

Article Type: Research Article

Abstract: With the rise in popularity of personal computers and decreasing cost, even a personal computer can execute complex and large calculations. So more researchers can invest in AI and machine learning. Humans can’t handle massive data sets or data that requires a long time to read and evaluate, whereas big data frameworks can read and analyze in a reasonable time. So relevant research has increased recently. In the social sciences, machine learning is used to forecast future trends and the index trend. Keeping up with current events is crucial nowadays to debate countermeasures in time. This study combines economic indicators …from 1988 to 2017 with leading indicators and other types of indicators. The recurrent neural network model predicts economic index trends and tests multiple variables. The proposed methods measure the error in predicting future trends in different models to learn which indicators work well together. Show more

Keywords: Machine learning, leading indicator, recurrent neural network, long short term memory, forecast trend

DOI: 10.3233/JIFS-219317

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2179-2189, 2022

Authors: Moghadam, Arman Balali | Rafsanjani, Marjan Kuchaki | Balas, Valentina Emilia

Article Type: Research Article

Abstract: This study takes a new perspective on the procedural content generation (PCG) evaluation problem, extracts current PCG evaluation methods from previous works, and presents a novel classification of these methods while showing each method’s capabilities. Also, the present study introduces a novel concept called Panda Evaluation. Additionally, the soft and hard launches were presented as two evaluation methods and possible building blocks of PE. A group of papers was analyzed to understand previous works and find new opportunities. In doing so, some missing PCG evaluation areas were found, and some new methods were proposed for future PCG evaluations. To the …best of our knowledge, this is the first time these concepts have been presented in PCG evaluation. Show more

Keywords: Procedural content generation (PCG), platformer, soft launch, panda evaluation (PE), machine learning (ML), evaluation graph

DOI: 10.3233/JIFS-219318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2191-2210, 2022

Authors: Tran, Duc-Nghia | Phi Khanh, Phung Cong | Solanki, Vijender Kumar | Tran, Duc-Tan

Article Type: Research Article

Abstract: Modern methods of monitoring help cow farmers save significantly monitoring time and improve cow health care efficiency. Behavioral changes when cows are sick may include increased or decreased daily activities such as increased lying or decreased walking time. Accelerometer advantages are low power consumption, small size, and lightweight. Thus, accelerometers have been widely used to monitor cow behavior. A cow monitoring system usually includes a central processor for receiving and processing information according to a behavioral classification algorithm through the cows’ movements. This paper introduces an effective classification system for Southern Yellow cow behavior using three degrees of freedom (3-DoF) …accelerometers. The proposed classifier applied GBDT algorithm (16 seconds window) with five features, offers the good performance while investigating with four Southern Yellow cattle. The classification achievement was assessed and compared to existing ones regarding sensitivity, accuracy, and positive predictive value. Show more

Keywords: 3-DoF, accelerometer, behavior, cow, classification

DOI: 10.3233/JIFS-219319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2211-2218, 2022