Search results for: neural activity

Authors: Aydin Teymourifar, Gurkan Ozturk, Ozan Bahadir

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In this paper, a new neural network based variable neighborhood search is proposed for the multi-objective dynamic, flexible job shop scheduling problems. The neural network controls the problems' constraints to prevent infeasible solutions, while the Variable Neighborhood Search (VNS) applies moves, based on the critical block concept to improve the solutions. Two approaches are used for managing the constraints, in the first approach, infeasible solutions are modified according to the constraints, after the moves application, while in the second one, infeasible moves are prevented. Several neighborhood structures from the literature with some modifications, also new structures are used in the VNS. The suggested neighborhoods are more systematically defined and easy to implement. Comparison is done based on a multi-objective flexible job shop scheduling problem that is dynamic because of the jobs different release time and machines breakdowns. The results show that the presented method has better performance than the compared VNSs selected from the literature.

Keywords: constrained optimization, neural network, variable neighborhood search, flexible job shop scheduling, dynamic multi-objective optimization

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Authors: Kinza Khan, Dad Muhmmad, Asif Saleem, Nadia Mukhtar, Tahir Yaqub

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Ethomedicines are more commonly used in underdeveloped and developing countries. These medicines are sometimes more potent in controlling microbial infections than conventional medicines. Medicinal plants have been common practice to cure many diseases for centuries. Murraya koenigii is one of these plants and is commonly used in South Asian countries as a flavoring agent in food. To evaluate its anti-microbial activity, six different bacterial strains (Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Salmonella typhi, Bacillus cereus and Klebsiella pneumonia were used. N-hexane extract of Murraya koenigii leaves shows maximum activity against Bacillus cereus. Acetone extract of Murraya koenigii shoots showed more efficient activity against Pseudomonas aeruginosa Dichloromethane extracts showed maximum activity against Bacillus cereus. Ethanol extract exhibited maximum activity against Pseudomonas aeruginosa and Klebsiella pneumoniae. The methanol extract of Murraya koenigii shoots displayed maximum antibacterial activity against Bacillus cereus. Antifungal activity Ethanol extract was more effective against Candida albicans.

Keywords: ethnomedicines, bacteria, fungi, murraya koenigii, antimicrobial activity

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Authors: Isaac Yeboah, Emmanuel Osei Sarpong

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Physical activity (PA) is considered the best preventative tool for maintaining physical health and extending lifespan because it offers a range of benefits. However, for people to benefit from physical activity, it must be done at a level that can enhance fitness, health, and overall well-being. Therefore, this study aimed to determine the link between physical activity participation levels and cardiorespiratory fitness (CRF). The study employed the descriptive correlational design. The census sampling technique included all 190-teaching staff from the two senior high schools in the Dormaa East District. An adapted International Physical Activity Questionnaire (IPAQ) and standardized physical fitness tests were used for data collection. Descriptive statistics of frequency counts and percentages were used to answer the research questions while inferential statistics of Pearson Product Moment Correlation and the independent sample t-test were used to test hypotheses one and two respectively. A moderately negative (r = -.156) relationship existed between physical activity participation levels and CRF. Female teachers demonstrated a statistically significant value (p = .045) than males. The study concluded that the reported physical activity levels of the teachers did not reflect their CRF. The implication is that either the participants overestimated their physical activity participation levels, or the intensity of the activities was not high enough to affect physiological changes. For the teachers' CRF to improve, it was suggested, among other things, that regular physical activity sessions of the appropriate mixes of FITT be organized.

Keywords: physical activity, participation levels, cardiorespiratory fitness, sedentary lifestyle, teachers

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Authors: Pun Jankrajangjaeng

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Senna garrettiana is a climatic tropical plant in Southeast Asia. Senna garrettiana (Craib) is used as a medicinal plant in Thailand, in which the experiment reported that the plant contains triterpenoids, ligans, phenolics, and fungal metabolites. Thus, it is also reported that the plant possesses interesting biological activity such as antioxidant activity. Therefore, Senna garrettiana is selected to examine the antimicrobial activity. The purpose of this study is to examine the antimicrobial activity of Senna garrettiana (crab) extract against Gram-positive Staphylococcus aureus and Gram-negative Salmonella typhi, and the fungus Candida albicans. This study performed the agar disk-diffusion method and broth microdilution by using five concentrations of plant extract to determine the minimum inhibitory concentration (MIC) of S. garrettiana extract. The result showed that S. garrettiana extract gave the maximum zone inhibition of 11.7 mm, 13.7 mm, and 14.0 mm against S. aureus, S. typhi, and C. albicans, respectively. The MIC value of S. garrettiana against S. aureus was 125 µg/mL while the MIC in S. typhi and C. albicans greater than 2000 µg/mL. To conclude, S. garrettiana extract showed higher sensitivity of antibacterial activity against gram-positive bacteria than gram-negative bacteria. In addition, the plant extracts also possessed antifungal activity. Therefore, further investigation to confirm the mechanism of action of antimicrobial activity in S. garrettiana extract should be performed to identify the target of the antimicrobial action.

Keywords: antimicrobial activity, Candida albicans, Salmonella typhi, Senna garrettiana, Staphylococcus aureus

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Authors: Messaouda Belaid, Salima Kebbouche-Gana, Djamila Benaziza

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Our study focuses on determining the antibacterial activity of some honeys from northern Algeria. To test this activity, the agar well diffusion methods was employed. The bacterial strains tested were Staphylococcus aureus, Bacillus subtilis, Streptococcus faecalis, Klebsiella pneumoniae, Escherichia coli, and Pseudomonas aeroginosae. The results showed that all the microbes tested were inhibited by all honey used in this study but Those bacteria that appear to be more sensitive to all honey tested are Staphylococcus aureus and Pseudomonas aeroginosae.

Keywords: honey, antibacterial activity, Northern Algeria, Staphylococcus aureus

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Stylianos Kampakis

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This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

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Authors: Parya Tabei, Maryam Habibifar

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Decision-making is an important cognitive function that can be defined as the process of choosing an option among available options to achieve a specific goal. Consumer ‘need’ is the main reason for purchasing decisions. Human decision-making while buying products online is subject to various factors, one of which is the quality and effect of advertising photos. Advertising photo editing can have a significant impact on people's virtual purchase decisions. This technique helps improve the quality and overall appearance of photos by adjusting various aspects such as brightness, contrast, colors, cropping, resizing, and adding filters. This study, by examining the effect of editing advertising photos on the virtual purchase decision using EEG data, tries to investigate the effect of edited images on the decision-making of customers. A group of 30 participants were asked to react to 24 edited and unedited images while their EEG was recorded. Analysis of the EEG data revealed increased alpha wave activity in the occipital regions (O1, O2) for both edited and unedited images, which is related to visual processing and attention. Additionally, there was an increase in beta wave activity in the frontal regions (FP1, FP2, F4, F8) when participants viewed edited images, suggesting involvement in cognitive processes such as decision-making and evaluating advertising content. Gamma wave activity also increased in various regions, especially the frontal and parietal regions, which are associated with higher cognitive functions, such as attention, memory, and perception, when viewing the edited images. While the visual processing reflected by alpha waves remained consistent across different visual conditions, editing advertising photos appeared to boost neural activity in frontal and parietal regions associated with decision-making processes. These Findings suggest that photo editing could potentially influence consumer perceptions during virtual shopping experiences by modulating brain activity related to product assessment and purchase decisions.

Keywords: virtual purchase decision, advertising photo, EEG parameters, decision Making

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Yichao Ma, Chengsiong Chin, Wailok Woo

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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and three-dimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who is the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: hdd noise, jury test, neural network model, psychoacoustic annoyance

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Authors: A. Shebani, C. Pislaru

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Wear of materials is an everyday experience and has been observed and studied for long time. The prediction of wear is a fundamental problem in the industrial field, mainly correlated to the planning of maintenance interventions and economy. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminium. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, and the alicona was used to measure the volume loss for pin and disc. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling and the simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

Keywords: wear modelling, Archard Model, ASTM Model, Neural Networks Model, Pin-on-disc Test, Talysurf, digital microscope, Alicona

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Nantawan Soonklang, Linda Chularojanamontri, Urarat Nanna

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Ethnopharmacological relevance: Thunbergia laurifolia Lindl. belongs to the family Acanthaceae commonly known as Rang jeud in Thailand. This plant is traditionally used in Thailand for centuries as an antidote for several poisons and drug overdose. Aim of the study: This research aimed to study the analgesic and antipyretic activities of T. laurifolia water extract by using animal models. Materials and Methods: The analgesic activity was studied using 2 methods of pain induction including acetic acid and heat induced pain. And the antipyretic activity study was performed by yeast-induced hyperthermia. Results: The results showed that the administration of T. laurifolia extract possessed analgesic activity by reducing acetic acid-induced writhing response and heat-induced pain as well as showed antipyretic activity by decreasing body temperature of hyperthermic rats induced by brewer’s yeast. Conclusion: The study indicates that the T. laurifolia extract possesses analgesic and antipyretic activities in animals.

Keywords: Thunbergia laurifolia extract, analgesic activity, antipyretic activity, hyperthermia

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Authors: Ruhollah Basatnia, Ali Reza Aghababa, Mehrdad Anbarian, Sara Akbari, Mohammad Khazaee

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Introduction: The investigation of brain activity in sports has become a subject of interest for researchers. Several studies have examined the patterns or differences in brain activity during different sports situations. Previous studies have suggested that the pattern of cortical activity may differ between different football positions, such as goalkeepers and other players. This study aims to investigate the differences in electroencephalographic (EEG) activity between the positions of goalkeeper and forward in professional football players. Methods: Fourteen goalkeepers and twelve forwards, all males between 19-28 years old, participated in the study. EEG activity was recorded while participants were sitting with their eyes closed for 5 minutes. The mean relative power of EEG activity for each frequency band was compared between the two groups using independent samples t-test. Findings: The study found significant differences in the relative power of EEG activity between different frequency bands and electrodes. Notably, significant differences were observed in the mean relative power of EEG activity between the two groups for certain frequency bands and electrodes. These findings suggest that EEG activity can serve as a sensory indicator for cognitive and performance differences between goalkeepers and forwards in football players. Discussion: The results of this study suggest that EEG activity can be used to identify cognitive and performance differences between goalkeepers and forwards in football players. However, further research is needed to establish the relationship between EEG activity and actual performance in the field. Future studies should investigate the potential influence of other factors, such as fatigue and stress, on the EEG activity of football players. Additionally, the use of real-time EEG feedback could be explored as a tool for training and performance optimization in football players. Further research is required to fully understand the potential of EEG activity as a sensory indicator for cognitive and performance differences between football player positions and to explore its potential applications for training and performance optimization in football and other sports.

Keywords: football, brain activity, EEG, goalkeepers, forwards

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Authors: Rafael Marinho de Andrade, Elcio Hideti Shiguemori, Rafael Duarte Coelho dos Santos

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Relying on computer vision, many clever things are possible in order to make the world safer and optimized on resource management, especially considering time and attention as manageable resources, once the modern world is very abundant in cameras from inside our pockets to above our heads while crossing the streets. Thus, automated solutions based on computer vision techniques to detect, react, or even prevent relevant events such as robbery, car crashes and traffic jams can be accomplished and implemented for the sake of both logistical and surveillance improvements. In this paper, we present an approach for vehicles’ abnormal behaviors detection from highway footages, in which the vectorial data of the vehicles’ displacement are extracted directly from surveillance cameras footage through object detection and tracking with a deep convolutional neural network and inserted into a long-short term memory neural network for behavior classification. The results show that the classifications of behaviors are consistent and the same principles may be applied to other trackable objects and scenarios as well.

Keywords: artificial intelligence, behavior detection, computer vision, convolutional neural networks, LSTM, highway footage

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Hala Zaghloul, Taymoor Nazmy

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One of the great challenges of the 21st century is to build a robot that can perceive and act within its environment and communicate with people, while also exhibiting the cognitive capabilities that lead to performance like that of people. The Pulse Coupled Neural Network, PCNN, is a relative new ANN model that derived from a neural mammal model with a great potential in the area of image processing as well as target recognition, feature extraction, speech recognition, combinatorial optimization, compressed encoding. PCNN has unique feature among other types of neural network, which make it a candid to be an important approach for perceiving in cognitive systems. This work show and emphasis on the potentials of PCNN to perform different tasks related to image processing. The main drawback or the obstacle that prevent the direct implementation of such technique, is the need to find away to control the PCNN parameters toward perform a specific task. This paper will evaluate the performance of PCNN standard model for processing images with different properties, and select the important parameters that give a significant result, also, the approaches towards find a way for the adaptation of the PCNN parameters to perform a specific task.

Keywords: cognitive system, image processing, segmentation, PCNN kernels

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: Abdulhakeem Sulyman, Olukotun Zainab, Hammed Abdulquadri

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Lipases (triacylglycerol acyl hydrolases) (EC 3.1.1.3) are class of enzymes that catalyses the hydrolysis of triglycerides to glycerol and free fatty acids. They account for up to 10% of the enzyme in the market and have a wide range of applications in biofuel production, detergent formulation, leather processing and in food and feed processing industry. This research was conducted to study the effect of some metal ions on the activity of purified lipase produced by Aspergillus niger cultured on Vitellaria paradoxa shells. Purified lipase in 12.5 mM p-NPL was incubated with different metal ions (Zn²⁺, Ca²⁺, Mn²⁺, Fe²⁺, Na⁺, K⁺ and Mg²⁺). The final concentrations of metal ions investigated were 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0 mM. The results obtained from the study showed that Zn²⁺, Ca²⁺, Mn²⁺ and Fe²⁺ ions increased the activity of lipase up to 3.0, 3.0, 1.0, and 26.0 folds respectively. Lipase activity was partially inhibited by Na⁺ and Mg²⁺ with up to 88.5% and 83.7% loss of activity respectively. Lipase activity was also inhibited by K⁺ with up to 56.7% loss in the activity as compared to in the absence of metal ions. The study concluded that lipase produced by Aspergillus niger cultured on Vitellaria paradoxa shells can be activated by the presence of Zn²⁺, Ca²⁺, Mn²⁺ and Fe²⁺ and inhibited by Na⁺, K⁺ and Mg²⁺.

Keywords: Aspergillus niger, Vitellaria paradoxa, lipase, metal ions

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Authors: K. M. Somawathie, V. Rizliya, H. A. M. Wickrmasinghe, Terrence Madhujith

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The main objective of our study was to determine the in vitro antioxidant and DNA protectant activity of aqueous extract of S. melongena with different skin colors; dark purple (DP), moderately purple (MP), light purple (LP) and purple and green (PG). The antioxidant activity was evaluated using the DPPH and ABTS free radical scavenging assay, ferric reducing antioxidant power (FRAP), ferric thiocyanate (FTC) and the egg yolk model. The effectiveness of eggplant extracts against radical induced DNA damage was also determined. There was a significant difference (p < 0.0001) between the skin color and antioxidant activity. TPC and FRAP values of eggplant extracts ranged from 48.67±0.27-61.11±0.26 (mg GAE/100 g fresh weight) and 4.19±0.11-7.46±0.26 (mmol of FeS04/g of fresh weight) respectively. MP displayed the highest percentage of DPPH radical scavenging activity while, DP demonstrated the strongest total antioxidant capacity. In the FTC and egg yolk model, DP and MP showed better antioxidant activity than PG and LP. All eggplant extracts showed potent antioxidant activity in retaining DNA against AAPH mediated radical damage. DP and MP demonstrated better antioxidant activity which may be attributed to the higher phenolic content since a positive correlation was observed between the TPC and the antioxidant parameters.

Keywords: Solanum melongena, skin color, antioxidant, DNA protection, lipid peroxidation

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Authors: S. Nikpour, S. Vahidi, H. Haghani

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Introduction: Exercise has mental and physical health benefits for patients with advanced stage cancer who actively receive chemotherapy, yet little is known about patients’ levels of interest in becoming more active or their confidence in increasing their activity level. Methods and materials: A convenience sample of 200 patients with advanced-stage cancer who were receiving chemotherapy completed self-report measures assessing physical activity level, mood, and quality-of-life variables. Qualitative data on patient-perceived benefits of, and barriers to, physical activity also were collected, coded by independent raters, and organized by predominant themes. Results: Current physical activity level, physical activity outcome expectations, and positive mood were significantly associated with self-efficacy. Fatigue was the most frequently listed barrier to physical activity; improved physical strength and health were the most commonly listed benefits. Participants identified benefits related to both general health and cancer-symptom management that were related to exercise. 59.5% of participants reported that they were seriously planning to increase or maintain their physical activity level, and over 40% reported having interest in receiving an intervention to become more active. Conclusion: These results suggested that many advanced-stage cancer patients who receive chemotherapy are interested in maintaining or increasing their physical activity level and in receiving professional support for exercise. In addition, these individuals identified general health and cancer-specific benefits of, and barriers to, physical activity. Future research will investigate how these findings may be incorporated into physical activity interventions for advanced-stage oncology patients receiving medical treatment.

Keywords: physical activity, cancer, self-efficacy

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Said Benkaciali, Mourad Haddadi, Abdallah Khellaf, Kacem Gairaa, Mawloud Guermoui

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In this study, many experiments were carried out to assess the influence of the input parameters on the performance of multilayer perceptron which is one the configuration of the artificial neural networks. To estimate the daily global solar radiation on the horizontal surface, we have developed some models by using seven combinations of twelve meteorological and geographical input parameters collected from a radiometric station installed at Ghardaïa city (southern of Algeria). For selecting of best combination which provides a good accuracy, six statistical formulas (or statistical indicators) have been evaluated, such as the root mean square errors, mean absolute errors, correlation coefficient, and determination coefficient. We noted that multilayer perceptron techniques have the best performance, except when the sunshine duration parameter is not included in the input variables. The maximum of determination coefficient and correlation coefficient are equal to 98.20 and 99.11%. On the other hand, some empirical models were developed to compare their performances with those of multilayer perceptron neural networks. Results obtained show that the neural networks techniques give the best performance compared to the empirical models.

Keywords: empirical models, multilayer perceptron neural network, solar radiation, statistical formulas

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Authors: Rachid Dehini, Brahim Berbaoui

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The main objectives of shunt active power filter (SAPF) is to preserve the power system from unwanted harmonic currents produced by nonlinear loads, as well as to compensate the reactive power. The aim of this paper is to present a (PAPF) supplied by the Photovoltaic cells ,in such a way that the (PAPF) feeds the linear and nonlinear loads by harmonics currents and the excess of the energy is injected into the power system. In order to improve the performances of conventional (PAPF) This paper also proposes artificial neural networks (ANN) for harmonics identification and DC link voltage control. The simulation study results of the new (SAPF) identification technique are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: SAPF, harmonics current, photovoltaic cells, MPPT, artificial neural networks (ANN)

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Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

Procedia PDF Downloads 184

Authors: Carl J. Caspersen, Kenneth E. Powell, Gregory M. Christenson, Kirupa V. Patel

Abstract:

‘Physical activity’, ‘exercise’, and ‘physical fitness’ are terms that describe different concepts. However, they are often confused with one another, and the terms are sometimes used interchangeably. This paper proposes definitions to distinguish them. Physical activity is defined as any bodily movement produced by skeletal muscles that result in energy expenditure. The energy expenditure can be measured in kilocalories. Physical activity in daily life can be categorized into occupational, sports, Conditioning, household, or other activities. Exercise is a subset of physical activity that is planned, structured, and repetitive and has as a final or an intermediate objective the improvement or maintenance of physical fitness. Physical fitness is a set of attributes that are either health- or skill-related. The degree to which people have these attributes can be measured with specific tests. These definitions are offered as an interpretational framework for comparing studies that relate physical activity, exercise, and physical fitness to health. Physical activity is defined as any bodily movement produced by skeletal muscles that require energy expenditure. Physical inactivity has been identified as the fourth leading risk factor for global mortality causing an estimated 3.2 million deaths globally. Regular moderate intensity physical activity – such as walking, cycling, or participating in sports – has significant benefits for health. For instance, it can reduce the risk of cardiovascular diseases, diabetes, colon and breast cancer, and depression. Moreover, adequate levels of physical activity will decrease the risk of a hip or vertebral fracture and help control weight. Any bodily movement produced by the contraction of skeletal muscle that increases energy expenditure above a basal level. In these guidelines, physical activity generally refers to the subset of physical activity that enhances health.

Keywords: physical activity, exercise, physical fitness, sports

Procedia PDF Downloads 355

Authors: Hanan H. ALQahtani, Karen Jones

Abstract:

Catching a ball during sitting is a functional exercise commonly used in rehabilitation to enhance trunk muscle activity. To progress this exercise, physiotherapists incorporate a Swiss ball or change seat height. However, no study has assessed the effect of different seating surfaces on trunk muscle activity while catching a ball. Objective: To investigate the effect of catching a ball during sitting on a Swiss ball, a low seat and a high seat on trunk muscle activity. Method: A repeated-measures, counterbalanced design was used. A total of 26 healthy participants (15 female and 11 male) performed three repetitions of catching a ball on each seating surface. Using surface electromyography (sEMG), the activity of the bilateral transversus abdominis/internal oblique (TrA/IO), rectus abdominis (RA), erector spinae (ES) and lumbar multifidus (MF) was recorded. Trunk muscle activity was normalized using maximum voluntary isometric contraction and analyzed. Statistical significance was set at p ≤ .05. Results: No significant differences were observed in the activity of RA, TrA/IO, ES or MF between a low seat and a Swiss ball. However, the activity of the right and left ES on a low seat was significantly greater than on a high seat (p = .017 and p = .017, respectively). Conversely, the activity of the right and left RA on a high seat was significantly greater than on a low seat (p = .007 and p = .004, respectively). Conclusion: This study suggests that replacing a low seat with a Swiss ball while catching a ball is insufficient to increase trunk muscle activity, whereas changing the seat height could induce different trunk muscle activities. However, research conducted on patients is needed before translating these results into clinical settings.

Keywords: catching, electromyography, seating, trunk

Procedia PDF Downloads 283