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ZHU, JINGBO, MATTHEW Y. MA, JINHONG K. GUO, and ZHENXING WANG. "CONTENT CLASSIFICATION AND RECOMMENDATION TECHNIQUES FOR VIEWING ELECTRONIC PROGRAMMING GUIDE ON A PORTABLE DEVICE." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 02 (March 2007): 375–95. http://dx.doi.org/10.1142/s0218001407005399.
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With the merge of digital television (DTV) and the exponential growth of broadcasting network, an overwhelmingly amount of information has been made available to a consumer's home. Therefore, how to provide consumers with the right amount of information becomes a challenging problem. In this paper, we propose an electronic programming guide (EPG) recommender based on natural language processing techniques, more specifically, text classification. This recommender has been implemented as a service on a home network that facilitates the personalized browsing and recommendation of TV programs on a portable remote device. Evaluations of our Maximum Entropy text classifier were performed on multiple categories of TV programs, and a near 80% retrieval rate is achieved using a small set of training data.
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Abreu-Lima, C., P. Arnaud, C. R. Brohet, B. Denis, J. Gehring, I. Graham, G. van Herpen, et al. "Evaluation of ECG Interpretation Results Obtained by Computer and Cardiologists." Methods of Information in Medicine 29, no. 04 (1990): 308–16. http://dx.doi.org/10.1055/s-0038-1634794.
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AbstractIn an international project investigators from 25 institutes are trying to establish a common reference library and evaluation methods for testing the diagnostic performance of various ECG computer programs and of cardiologists, based on ECG-independent clinical information. A first set of 500 validated ECGs was collected and analyzed by fifteen different computer programs and nine cardiologists, seven of who analysed the ECG and five the VCG. A coding scheme was used to map individual diagnostic statements onto a common set. Combined program and referee results were obtained by weighted averaging. Preliminary results indicate that the classification accuracy of several programs can still be improved. However, it was also apparent that the results of the best 12-lead ECG computer programs proved to be almost as accurate as the best of seven cardiologists in classifying seven main disease categories, i.e., normal, left, right and biventricular hypertrophy, anterior, inferior and combined myocardial infarction. Evaluation of rhythm statements and conduction disturbances was not included in the study. The data collection is still being pursued in order to reach over 1,000 cases. In this way a common diagnostic database is being established for comparative testing of diagnostic computer programs. This should lead to consumer protection and improve the accuracy and reliability of computerized electrocardiography.
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Kumar C, Udhaya, Miruthula R C, Pavithra G, Revathi R, and Suganya M. "FPGA-based Hardware Acceleration for Fruit Recognition Using SVM." Irish Interdisciplinary Journal of Science & Research 06, no. 02 (2022): 22–29. http://dx.doi.org/10.46759/iijsr.2022.6204.
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Selection classification for Fruit recognition could be an absolute zone of inspection. Fruit Recognition mistreatment FPGA-based Hardware Acceleration by SVM is helpful for the observance and indexing of the fruits consistent with their kind with the peace of mind of a quick production chain. During this test, we have processed to initial replacement prime quality data-set of pictures grouped in the 5 preferred varieties of oval-shaped fruits. Honor to the fast image process techniques for the development, image resolution, quality of the algorithms leads to carry-out image process and computational tasks. In recent years, deep neural networks have a diode to the event of the many new applications associated with preciseness agriculture, as well as fruit recognition. An algorithm consumes computer power and memory, which has a significant impact on standard and performance, especially when working with large image datasets. Within the planned work, FPG is A based mostly on hardware acceleration for fruit, and recognition is mistreatment with SVM. The Support Vector Machine could be a real-time machine learning tool meant for high predicted classification accuracy through the attributes mentioned. Using SVM for embedded system programs is incredibly difficult attributable to the intensive computations needed. This will increase the attractiveness of implementing SVM on hardware platforms for reaching performance computing with the demanded value of power consumption. Finally, a difficult trade-off between meeting embedded period systems constraints and high classification accuracy has been determined.
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Kumar C, Udhaya, Miruthula R C, Pavithra G, Revathi R, and Suganya M. "FPGA-based Hardware Acceleration for Fruit Recognition Using SVM." Irish Interdisciplinary Journal of Science & Research 06, no. 02 (2022): 22–29. http://dx.doi.org/10.46759/iijsr.2022.6204.
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Selection classification for Fruit recognition could be an absolute zone of inspection. Fruit Recognition mistreatment FPGA-based Hardware Acceleration by SVM is helpful for the observance and indexing of the fruits consistent with their kind with the peace of mind of a quick production chain. During this test, we have processed to initial replacement prime quality data-set of pictures grouped in the 5 preferred varieties of oval-shaped fruits. Honor to the fast image process techniques for the development, image resolution, quality of the algorithms leads to carry-out image process and computational tasks. In recent years, deep neural networks have a diode to the event of the many new applications associated with preciseness agriculture, as well as fruit recognition. An algorithm consumes computer power and memory, which has a significant impact on standard and performance, especially when working with large image datasets. Within the planned work, FPG is A based mostly on hardware acceleration for fruit, and recognition is mistreatment with SVM. The Support Vector Machine could be a real-time machine learning tool meant for high predicted classification accuracy through the attributes mentioned. Using SVM for embedded system programs is incredibly difficult attributable to the intensive computations needed. This will increase the attractiveness of implementing SVM on hardware platforms for reaching performance computing with the demanded value of power consumption. Finally, a difficult trade-off between meeting embedded period systems constraints and high classification accuracy has been determined.
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Chen, Wen-Kuo, Venkateswarlu Nalluri, Man-Li Lin, and Ching-Torng Lin. "Identifying Decisive Socio-Political Sustainability Barriers in the Supply Chain of Banking Sector in India: Causality Analysis Using ISM and MICMAC." Mathematics 9, no. 3 (January 26, 2021): 240. http://dx.doi.org/10.3390/math9030240.
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The banking sector often plays a crucial role in the improvement of infrastructure and economy of any country. In many emerging economies, it is apparent that a wide variety of social and political issues are related to the associated supply chain sustainability of financial service firms. Although such sustainability and its implementation issues have largely been addressed in existing research literature and in practice for many years, the attention towards socio-political sustainability aspects has been quite limited. Thus, this study attempted to explore the determinants for improving socio-political sustainability in financial service firms. Through adopting the fuzzy Delphi method (FDM), performing an exhaustive literature review, and conducting semi-structured interviews with the decision-makers of the service firms, nine key barriers for socio-political sustainability were first identified in this study. Then, the influence relationships of the key barriers were assessed by 15 experts. During the assessment process, the interrelationships and their dependence powers among key barriers were analyzed using the interpretive structural modelling (ISM) approach and cross-impact matrix multiplication applied to classification (MICMAC) methods. The assessment results show that among the studied barriers, “antisocial considerations”, “unstable political climate”, and “lack of political coherence” are the decisive barriers that affect the socio-political sustainability in the supply chain of financial service firms. The knowledge in understanding and reducing these decisive barriers can provide service sector practitioners, especially those with limited resources, the enhanced capability to conduct better planning and designing of effective and continuous improvement programs, so as to win over new consumers and retain existing clients by offering sustainable services.
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Hatalis, Kostas, Chengbo Zhao, Parv Venkitasubramaniam, Larry Snyder, Shalinee Kishore, and Rick S. Blum. "Modeling and Detection of Future Cyber-Enabled DSM Data Attacks." Energies 13, no. 17 (August 21, 2020): 4331. http://dx.doi.org/10.3390/en13174331.
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Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.
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Dalay, Jacobson B., and Fershie Yap. "CONSUMERS’ AWARENESS ON JOLLIBEE FOODS CORPORATION’S CORPORATE SOCIAL RESPONSIBILITY PROGRAMS AND THEIR BUYING BEHAVIOR TOWARDS A STRATEGIC CORPORATE SOCIAL RESPONSIBILITY." International Journal of Engineering Technologies and Management Research 8, no. 4 (April 14, 2021): 25–39. http://dx.doi.org/10.29121/ijetmr.v8.i4.2021.895.
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The research identified the consumers’ awareness of CSR, consumers’ buying behavior relating to Jollibee Foods Corporation’s (JFC) Corporate Social Responsibility (CSR) programs as well as to JFC as an organization, determined the relationship between consumers’ CSR awareness and consumer buying behavior, and ascertained differences in consumer buying behavior according to their socio-demographic classification. The study used descriptive research design, using self-administered questionnaires through purposive sampling method in the selection of respondents with a sample size of 250 consumers who participated in the survey. Data were statistically treated using mean, frequency, and percent distribution and standard deviation, chi-square, correlation, Kruskal-Wallis, and Mann-Whitney. Based on the findings, the researcher concludes that 25-34 years old consumers prefer Jollibee Foods brands as their fast-food preference. Females, those with Bachelor’s degrees, and are employed prefer JFC as well. The majority of respondents/consumers have knowledge on the topic at hand, thus they could be seen as the current audience of the CSR programs being done by JFC. It is highly commendable that JFC is conducting programs for the benefit of its targeted segments and communities, but consumers are not fully aware of these programs. Overall, consumers are most aware of other CSR programs done by JFC. Therefore, the researcher recommends creating a strategic CSR communication of JFC’s CSR programs to increase the awareness of consumers. It should capitalize on identified significant relationships between consumers’ CSR awareness and buying behavior to gain economic advantage.
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Ion, I., R. Arhire, and M. Macesanu. "Programs complexity: comparative analysis hierarchy, classification." ACM SIGPLAN Notices 22, no. 4 (April 1987): 94–102. http://dx.doi.org/10.1145/24714.24726.
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Dimitrieska, Savica, and Tanja Efremova. "LOYALTY PROGRAMS: DO COMPANIES REALLY MAKE CONSUMERS LOYAL?" Entrepreneurship 9, no. 2 (November 10, 2021): 23–32. http://dx.doi.org/10.37708/ep.swu.v9i2.2.
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In the markets of different products and services, the interests of both, companies and consumers collide. Companies that offer products and services expect a return on investment and higher profits. They can achieve these goals only if they have regular consumers, preferably loyal who will buy products and services more often or in larger quantities. The companies are interested in achieving long-term and sustainable relationships with the consumers and they want to minimize the churn and their switching to competitors. Consumers, on the other hand, have more sophisticated demands and expect more value for less money. They are interested not only in the quality of the product, but also in packaging, discounts, cashback, rewards, additional customer services, free shipping, maintenance, special treatments, reputation, etc. To meet these consumer expectations, companies offer many promotional activities, including loyalty programs. Loyalty programs, as part of the CRM (customer relationship management), are marketing programs that reward consumers for their repeated purchases over a longer period of time. Unlike other promotional tools, loyalty programs need to build a long-term relationship between companies and their consumers. But despite the offer of such programs, many studies show that consumers are not loyal and do not fully enjoy the benefits of the programs. Do companies give “something for nothing” and spend their money in vain? What are the reasons for the downfall of loyalty programs? This paper aims to investigate the reasons for consumer disloyalty despite the offered companies' loyalty programs. The paper will also provide guidance for companies to design loyalty programs that will attract more consumers.
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Shi, Yong, YingJie Tian, XiaoJun Chen, and Peng Zhang. "Regularized multiple criteria linear programs for classification." Science in China Series F: Information Sciences 52, no. 10 (October 2009): 1812–20. http://dx.doi.org/10.1007/s11432-009-0126-5.
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Al. R., Firend, and Wang Qian. "Marketing Strategies of Services and Purchasing Incentives in Asia." GATR Journal of Management and Marketing Review (JMMR) Vol. 3 (3) Jul-Sep 2018 3, no. 3 (September 29, 2018): 104–10. http://dx.doi.org/10.35609/jmmr.2018.3.3(2).
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Objective - This research explores the changing mechanism in the relationship between retailers and consumers whereby consumers face higher-prices due to inflation while their earnings, and thereby their disposal income, does not simultaneously increase. Methodology/Technique - An examination is conducted on the use of loyalty cards in the Malaysian retail sector to determine whether Asian consumers are enticed by the use of loyalty programs, which can be seen as an attempt to save money when making purchases. Findings - The findings suggest that loyalty programs will expand in the future to include other offerings as they gain momentum and popularity. This research concludes that Malaysian consumers, like most of Southeast Asian consumers, are price adverse, and hence will take opportunities to save money when making purchases. Novelty - The findings of this research can be generalized to the Southeast Asian region due to the similarity of consumption and national characteristics between Malaysian and Southeast Asian consumers. Type of Paper: Empirical. Keywords: Marketing; Strategy; Incentives; Services; Retail; Loyalty, Asia. JEL Classification: M30. M31. M39
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Vahid-Ghavidel, Morteza, Mohammad Sadegh Javadi, Matthew Gough, Sérgio F. Santos, Miadreza Shafie-khah, and João P. S. Catalão. "Demand Response Programs in Multi-Energy Systems: A Review." Energies 13, no. 17 (August 21, 2020): 4332. http://dx.doi.org/10.3390/en13174332.
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A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.
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Phillips, Charles D. "The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges." Health Services Insights 8 (January 2015): HSI.S35366. http://dx.doi.org/10.4137/hsi.s35366.
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Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges.
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Verma, Surendra P., and M. Abdelaly Rivera-Gómez. "Computer programs for the classification and nomenclature of igneous rocks." Episodes 36, no. 2 (June 1, 2013): 115–24. http://dx.doi.org/10.18814/epiiugs/2013/v36i2/005.
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Ganesan, Kamalanathan, João Tomé Saraiva, and Ricardo J. Bessa. "On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs." Energies 12, no. 14 (July 11, 2019): 2666. http://dx.doi.org/10.3390/en12142666.
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Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized.
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Polunina, Mariya M. "Click-Wrap Agreements for Computer Programs Distributed in the Internet." Juridical Science and Practice 16, no. 2 (2020): 67–73. http://dx.doi.org/10.25205/2542-0410-2020-16-2-67-73.
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The study identified several issues relating to click-wrap agreements in legislation and case law, namely: difficulty in reading terms of contract for an average user which results in users’ refusal to read them, and including onerous conditions by the rightholder. A range of measures to protect users’ rights from click-wrap agreements: consolidation of special conditions of click-wrap agreement invalidity, application of the rules relating to protection of a weak party of an agreement and consumers’ rights, application of the principle of good faith in case law.
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Giordana, A., and G. Lo Bello. "Learning Classification Programs: The Genetic Algorithm Approach." Fundamenta Informaticae 35, no. 1-4 (1998): 163–77. http://dx.doi.org/10.3233/fi-1998-35123409.
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Wang, Hu, Tianbao Liang, and Yanxia Cheng. "Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network." Mobile Information Systems 2021 (July 1, 2021): 1–7. http://dx.doi.org/10.1155/2021/5482662.
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Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.
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Panda, Debadrita, Sabyasachi Mukhopadhyay, and Rajarshi Saha. "BoPMLPIP: Application of Classification Techniques to Explore the Impact of PIP among BoPs." International Journal of Intelligent Systems and Applications 14, no. 6 (December 8, 2022): 13–27. http://dx.doi.org/10.5815/ijisa.2022.06.02.
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This study tries to gain insight into the effect of demographic and psychological variables on the Bottom of the Pyramid (BoP) consumers for making Packaging Influenced Purchase (PIP) decisions by focusing on two specific consumer behaviour theories - compensatory consumption and consumers’ resistance. Being the product's face, packaging contributes heavily to the above mentioned two streams of consumption behaviour. A collection of ten demographic variables and four psychological variables have been administered on a sample of 1400 BoP consumers to explore their effect behind making PIP of selected FMCG products. Various classification techniques have been deployed to capture the impact of these variables. This experimental research design revealed that both demographic and psychological variables affect the PIP. The comparison between urban and rural BoPs potentially comes with the guidelines for practical marketing implications.
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Fidan, Hüseyin. "Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce." Journal of theoretical and applied electronic commerce research 15, no. 1 (2020): 0. http://dx.doi.org/10.4067/s0718-18762020000100105.
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Johan, Elisa Belinda, and Aminuddin Rizal. "Allergen Recognition in Food Ingredients with Computer Vision." Ultima Computing : Jurnal Sistem Komputer 13, no. 2 (December 30, 2021): 44–49. http://dx.doi.org/10.31937/sk.v13i2.2051.
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The process of recognition and classification of food is very important. It can be useful for consumers who are sensitive in choosing foods that they want to consume. Considering that some food ingredients are allergens that can cause allergies for some people. This paper aims to design and build an Android-based system to detect food ingredients that can facilitate consumers in getting information about all allergens contained in the. The application is created by implementing Optical Character Recognition (OCR) algorithm and using Boyer Moore algorithm to do the word matching (string matching). The experiments were performed with trial of OCR, Boyer Moore, light sources, and technical words (uncommon words). Our experiment shows more than 90% accuracy obtained with different scenario applied.
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Apt, Krzysztof R., and Howard A. Blair. "Arithmetic Classification of Perfect Models of Stratified Programs." Fundamenta Informaticae 13, no. 1 (January 1, 1990): 1–17. http://dx.doi.org/10.3233/fi-1990-13103.
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We study here the recursion theoretic complexity of the perfect (Herbrand) models of stratified logic programs. We show that these models lie arbitrarily high in the arithmetic hierarchy. As a byproduct we obtain a similar characterization of the recursion theoretic complexity of the set of consequences in a number of formalisms for non-monotonic reasoning. We show that under some circumstances this complexity can be brought down to recursive enumerability.
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Chin, Wei-Ngan. "Safe fusion of functional expressions II: Further improvements." Journal of Functional Programming 4, no. 4 (October 1994): 515–55. http://dx.doi.org/10.1017/s0956796800001179.
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AbstractLarge functional programs are often constructed by decomposing each big task into smaller tasks which can be performed by simpler functions. This hierarchical style of developing programs has been found to improve programmers' productivity because smaller functions are easier to construct and reuse. However, programs written in this way tend to be less efficient. Unnecessary intermediate data structures may be created. More function invocations may be required.To reduce such performance penalties, Phil Wadler proposed a transformation algorithm, called deforestation, which could automatically fuse certain composed expressions together to eliminate intermediate tree-like data structures. However, his technique is currently safe (terminates with no loss of efficiency) for only a subset of first-order expressions.This paper will generalise the deforestation technique to make it safe for all first-order and higher-order functional programs. Our generalisation is explained using a model for safe fusion which views each function as a producer and its parameters as consumers. Through this model, syntactic program properties are proposed to classify producers and consumers as either safe or unsafe. This classification is used to identify sub-terms that can be safely fused/eliminated. We present the generalised transformation algorithm, illustrate it with examples and provide a termination proof for the transformation algorithm of first-order programs. This paper also contains a suite of additional techniques to further improve the basic safe fusion method. These improvements could be viewed as enhancements to compensate for some inadequacies of the syntactic analyses used.
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Rossinskaya, Elena, and Igor Ryadovskiy. "The Concept of Malware as a Means of Committing Computer Crimes: Classification and Methods of Illegal Use." Russian Journal of Criminology 14, no. 5 (November 20, 2020): 699–709. http://dx.doi.org/10.17150/2500-4255.2020.14(5).699-709.
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The authors analyze problems connected with malware from the standpoint of the doctrine of the methods of computer crimes/offenses as one of the components of the theory of information-computer support of criminalistic work. Most methods of computer crimes are based on the unauthorized access to computer facilities and systems gained through malware that, in fact, acts as a weapon of crime. The authors present a classification of malware based on different parameters: from the standpoint of criminal law and criminology; the standpoint of information technology; the standpoint of the doctrine of computer crimes/offenses. Various grounds for the classification of malware are examined. A general classification, widely used by the developers of antiviral software, includes virus-programs, worm-programs and trojan-programs. In the modern situation of massive digitization, it is not practical to regard masquerading as a legitimate file as a dominant feature of trojan software. On the contrary, criminals try hard to hide from the user the downloading, installation and activity of malware that cannot self-propagate. The key method of propagating trojan programs is sending mass emails with attachments masquerading as useful content. The classification of malware by the way and method of propagation - viruses, worms and trojan programs - is only currently used due to traditions and does not reflect the essence of the process. A different classification of malware into autonomous, semi-autonomous and non-autonomous programs is based on the possibility of their autonomous functioning. At present there is practically no malware whose functions include only one specific type of actions, most of it contains a combination of various types of actions implemented through module architecture, which offers criminals wide opportunities for manipulating information. The key mechanisms of malwares work are described and illustrated through examples. Special attention is paid to harmful encryption software working through stable cryptographic algorithms - ransomware, when criminals demand ransom for restoring data. There is no criminal liability for such theft. The authors outline the problems connected with the possibility of the appearance of new malware that would affect cloud resources.
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Lin, Ting-Yu, Hung-Tse Chan, Chih-Hsien Hsia, and Chin-Feng Lai. "Facial Skincare Products’ Recommendation with Computer Vision Technologies." Electronics 11, no. 1 (January 3, 2022): 143. http://dx.doi.org/10.3390/electronics11010143.
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Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate skincare products can exacerbate the condition of the skin. In view of this, this work proposes the use of computer vision (CV) technology to realize a new business model of facial skincare products. The overall framework is composed of a finger vein identification system, skincare products’ recommendation system, and electronic payment system. A finger vein identification system is used as identity verification and personalized service. A skincare products’ recommendation system provides consumers with professional skin analysis through skin type classification and acne detection to recommend skincare products that finally improve skin issues of consumers. An electronic payment system provides a variety of checkout methods, and the system will check out by finger-vein connections according to membership information. Experimental results showed that the equal error rate (EER) comparison of the FV-USM public database on the finger-vein system was the lowest and the response time was the shortest. Additionally, the comparison of the skin type classification accuracy was the highest.
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Afzalan, Milad, and Farrokh Jazizadeh. "Data-Driven Identification of Consumers With Deferrable Loads for Demand Response Programs." IEEE Embedded Systems Letters 12, no. 2 (June 2020): 54–57. http://dx.doi.org/10.1109/les.2019.2937834.
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Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Price-Based Demand Side Response Programs and Their Effectiveness on the Example of TOU Electricity Tariff for Residential Consumers." Energies 14, no. 2 (January 7, 2021): 287. http://dx.doi.org/10.3390/en14020287.
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Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.
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AbuBaker, Maher. "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine." Energies 12, no. 22 (November 11, 2019): 4287. http://dx.doi.org/10.3390/en12224287.
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This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.
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Jiang, Zigui, Rongheng Lin, and Fangchun Yang. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data." Energies 11, no. 9 (August 26, 2018): 2235. http://dx.doi.org/10.3390/en11092235.
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Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy.
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Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles." Energies 12, no. 22 (November 13, 2019): 4317. http://dx.doi.org/10.3390/en12224317.
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The concept of price elasticity of demand has been widely used for the assessment of the consumers’ behavior in the electricity market. As the residential consumers represent a significant percentage of the total load, price elasticity of their demand may be used to design desirable demand side response programs in order to manage peak load in a power system. The method presented in this study proposes an alternative approach towards price elasticity determination for zonal tariff users, based on comparisons of load profiles of consumers settled according to flat and time-of-use electricity tariffs. A detailed explanation of the proposed method is presented, followed by a case-study of price elasticity determination for residential electricity consumers in Poland. The forecasted values of price elasticity of demand for the Polish households using time-of-use (TOU) tariff vary between −1.7 and −2.3, depending on the consumers’ annual electricity consumption. Moreover, an efficiency study of residential zonal tariff is performed to assess the operation of currently applicable electricity tariffs. Presented analysis is based on load profiles published by Distribution System Operators and statistical data, but the method can be applied to the real-life measurements from the smart metering systems as well when such systems are accessible for residential consumers.
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Anglano, C., and M. Botta. "NOW G-Net: learning classification programs on networks of workstations." IEEE Transactions on Evolutionary Computation 6, no. 5 (October 2002): 463–80. http://dx.doi.org/10.1109/tevc.2002.800882.
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Morosan, Cristian, and Agnes DeFranco. "Classification and characterization of US consumers based on their perceptions of risk of tablet use in international hotels." Journal of Hospitality and Tourism Technology 10, no. 3 (September 17, 2019): 233–54. http://dx.doi.org/10.1108/jhtt-07-2018-0049.
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Purpose Cyber-attacks on hotel information systems could threaten the privacy of consumers and the integrity of the data they exchange upon connecting their mobile devices to hotel networks. As the perceived cyber-security risk may be reflected heterogeneously within the US consumer population traveling internationally, the purpose of this study is to examine such heterogeneity to uncover classes of US consumers based on their perceptions of risk of using tablets for various tasks when staying in hotels abroad. Design/methodology/approach Using data collected from 1,016 US consumers who stayed in hotels abroad, this study used latent profile analysis (LPA) to classify the consumers based on their perceptions of risk associated with several tablet use behaviors in hotels. Findings The analysis uncovered four latent classes and produced a characterization of these classes according to several common behavioral (frequency of travel, the continent of the destination, duration of stay and purpose of travel) and demographic (gender, age, income and education) consumer characteristics. Originality/value Being the first study that classifies consumers based on the risk of using tablets in hotels while traveling internationally, this study brings the following contributions: offers a methodology of classifying (segmenting) consumer markets based on their cyber-security risk perceptions, uses LPA, which provides opportunities for an accurate and generalizable characterization of multivariate data that comprehensively illustrate consumer behavior and broadens the perspective offered by the current literature by focusing on consumers who travel from their US residence location to international destinations.
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Joseph, Shibily, and E. A. Jasmin. "Demand response program for smart grid through real time pricing and home energy management system." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4558. http://dx.doi.org/10.11591/ijece.v11i5.pp4558-4567.
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Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.
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Jeong, Hyun-Ki, Hae-Kyung Kee, and Se-Hyun Park. "Priority Analysis for Consumers’ Purchasing Factors of Seafood Online Using AHP Method." Institute of Management and Economy Research 13, no. 3 (September 30, 2022): 449–61. http://dx.doi.org/10.32599/apjb.13.3.202209.449.
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Purpose The purpose of this study to explore factors consumers prioritize when purchasing seafood online. The originality of the study lies on adopting AHP-based approach in analyzing prioritized purchasing factors of seafood online. Design/methodology/approach A survey was conducted targeting Korean consumers who have purchased seafood online. AHP method was applied to rank factors consumers prioritize before making decision. Findings First, product’s factor ranked first among other high level factors including delivery service, seller, online platform. Second, sanitation, taste, country of origin ranked first, second, third respectively, within product’s factors. Third, safe delivery, timeliness, information accuracy ranked first, second, third respectively, within delivery factors. Fourth, consumer reviews, consumer response ability, promotion ranked first, second, third within seller factors. Fifth, Personal information management system, credibility, user-friendliness ranked first, second, third, within online platform factors. Research implications or Originality To activate seafood online market, it is crucial to assure consumers that the seafood is well managed in a sanitary way from the production site to table. Existing government programs such as seafood traceability system, HACCP, and cold-chain infrastructure needs improvement. Due to highly perishable characteristic of seafood , delivery factors matter when purchasing online. Online platforms needs to continue to improve delivery service. Seafood products are mostly not branded and without objective information about their properties. Creating quality classification and seafood brands are likely to help consumers chose seafood online.
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Heriyanto, Heriyanto, Ika Kurniawati, Fachri Amsury, Muhammad Rizki Fahdia, Irwansyah Saputra, Nanang Ruhyana, and Asrul. "Applied of Classification Technique in Data Mining For Credit Scoring." Inspiration: Jurnal Teknologi Informasi dan Komunikasi 12, no. 2 (December 31, 2022): 97–104. http://dx.doi.org/10.35585/inspir.v12i2.17.
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In the development of the banking business, credit issues remain interesting to study and uncover. Most of the problems occur not in the system implemented by the bank, but the problem occurs precisely in the human resources who manage credit, either in their relationship with consumers or in errors on the part of the bank which mispredicts in assessing consumers who apply for credit. Several studies in the computer field have been carried out to reduce credit risk which causes losses to the company. In this study, a comparison of the Naive Bayes, C4.5 and KNN algorithms was carried out which was applied to consumer data that received credit eligibility for good and bad customers. The best prediction results are nave Bayes with an accuracy of 95.95% and an AUC of 0.974. The results of this classification are implemented in the form of a website-based application that can be used to facilitate related parties in the credit scoring system.
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Faria, Pedro, João Spínola, and Zita Vale. "Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs." Energies 11, no. 8 (July 31, 2018): 1987. http://dx.doi.org/10.3390/en11081987.
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Distributed energy resources can contribute to an improved operation of power systems, improving economic and technical efficiency. However, aggregation of resources is needed to make these resources profitable. The present paper proposes a methodology for distributed resources management by a Virtual Power Player (VPP), addressing the resources scheduling, aggregation and remuneration based on the aggregation made. The aggregation is made using K-means algorithm. The innovative aspect motivating the present paper relies on the remuneration definition considering multiple scenarios of operation, by performing a multi-observation clustering. Resources aggregation and remuneration profiles are obtained for 2592 operation scenarios, considering 548 distributed generators, 20,310 consumers, and 10 suppliers.
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Scanlon, Alayne. "Blind and Visually Impaired Young Canadians S.C.O.R.E. at Computer Camp." Journal of Visual Impairment & Blindness 80, no. 5 (May 1986): 754–56. http://dx.doi.org/10.1177/0145482x8608000514.
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A monthly section highlighting the impact of technological development on consumers and the blindness/visual impairment field. We need your support. Please send news, reviews, and descriptions of new hardware, software, interfacing ideas, prototypes, training programs, job opportunities, volunteer usage, user evaluations, educational opportunities, exhibits—in short, anything and everything—to the Editor-in-Chief, Journal of Visual Impairment & Blindness, 15 W. 16th St., New York, NY 10011. In addition, we will attempt to direct reader queries to the appropriate experts, and publish questions and answers of general interest. Ideas and suggestions are welcomed.
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Czerwinski, Michael H. "Interfacing the Echo GP with the TRS-80 Color Computer." Journal of Visual Impairment & Blindness 80, no. 6 (June 1986): 812–14. http://dx.doi.org/10.1177/0145482x8608000610.
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A monthly section highlighting the impact of technological development on consumers and the blindness/visual impairment field. We need your support. Please send news, reviews, and descriptions of new hardware, software, interfacing ideas, prototypes, training programs, job opportunities, volunteer usage, user evaluations, educational opportunities, exhibits—in short, anything and everything—to the Editor-in-Chief, Journal of Visual Impairment & Blindness, 15 W. 16th St., New York, NY 10011. In addition, we will attempt to direct reader queries to the appropriate experts, and publish questions and answers of general interest. Ideas and suggestions are welcomed.
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Hosseini, Seyed Ali, Mehrdad Hojjat, and Azita Azarfar. "An integrated home energy management system by the load aggregator in a microgrid using the internet of things infrastructure." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6796. http://dx.doi.org/10.11591/ijece.v12i6.pp6796-6805.
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<span lang="EN-US">Smart technologies enable the significant participation of consumers in demand-side management programs. In this paper, the management of electrical energy consumption for a set of residential houses in a microgrid by a load aggregator for a 24-h planning horizon is studied. In this study, consumption management programs are implemented on controllable equipment by sending binary codes by the load aggregator via the internet of things (IoT) infrastructure to residential sockets. To increase the level of customer convenience and provide more flexibility for consumers to participate in demand response programs, a parameter called the value of lost load (VOLL) has been introduced. According to the results, in addition to no need to use the energy management system for each residential house, only by moving shiftable loads to off-peak hours, 18.34% of energy consumption costs are saved daily. Also, from the load aggregator’s viewpoint for every 10% change in status from normal to the scheduled priority, there is a reduction of about 3.4% in the consumer’s peak-load cost. If solar arrays and storage resources are used, more than 18% of the total consumption cost can be saved.</span>
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Alzahrani, Mohammad Eid, Theyazn H. H. Aldhyani, Saleh Nagi Alsubari, Maha M. Althobaiti, and Adil Fahad. "Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews." Computational Intelligence and Neuroscience 2022 (May 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/3840071.
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Most consumers rely on online reviews when deciding to purchase e-commerce services or products. Unfortunately, the main problem of these reviews, which is not completely tackled, is the existence of deceptive reviews. The novelty of the proposed system is the application of opinion mining on consumers’ reviews to help businesses and organizations continually improve their market strategies and obtain an in-depth analysis of the consumers’ opinions regarding their products and brands. In this paper, the long short-term memory (LSTM) and deep learning convolutional neural network integrated with LSTM (CNN-LSTM) models were used for sentiment analysis of reviews in the e-commerce domain. The system was tested and evaluated by using real-time data that included reviews of cameras, laptops, mobile phones, tablets, televisions, and video surveillance products from the Amazon website. Data preprocessing steps, such as lowercase processing, stopword removal, punctuation removal, and tokenization, were used for data cleaning. The clean data were processed with the LSTM and CNN-LSTM models for the detection and classification of the consumers’ sentiment into positive or negative. The LSTM and CNN-LSTM algorithms achieved an accuracy of 94% and 91%, respectively. We conclude that the deep learning techniques applied here provide optimal results for the classification of the customers’ sentiment toward the products.
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Medeiros, Erika Carlos, Leandro Maciel Almeida, and José Gilson de Almeida Teixeira Filho. "Computer Vision and Machine Learning for Tuna and Salmon Meat Classification." Informatics 8, no. 4 (October 19, 2021): 70. http://dx.doi.org/10.3390/informatics8040070.
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Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.
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Figueiredo, Marco A., Clay S. Gloster, Mark Stephens, Corey A. Graves, and Mouna Nakkar. "Implementation of Multispectral Image Classification on a Remote Adaptive Computer." VLSI Design 10, no. 3 (January 1, 2000): 307–19. http://dx.doi.org/10.1155/2000/31983.
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As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms is justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of magnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application that can benefit from implementation on an FPGA-based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm (implemented on a typical general-purpose computer).
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ZHANG, MENGJIE, and PHILLIP WONG. "EXPLICITLY SIMPLIFYING EVOLVED GENETIC PROGRAMS DURING EVOLUTION." International Journal of Computational Intelligence and Applications 07, no. 02 (June 2008): 201–32. http://dx.doi.org/10.1142/s1469026808002247.
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The genetic programming (GP) evolutionary process typically introduces a large amount of redundancy and unnecessary complexity into evolved programs. Quick growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Rather than implicitly controlling the redundancy and code growth/bloat as in most of the existing approaches, this paper investigates an algebraic simplification algorithm for explicitly removing the redundancy from the genetic programs and simplifying these programs online during the evolutionary process. The new GP system with the simplification is examined and compared with a standard GP system on two regression and three classification problems of varying difficulties. The results show that the GP system employing a simplification component can achieve superior efficiency with comparable or slightly superior effectiveness to the standard GP system on these problems. The programs evolved by the new GP approach with the explicit simplification contain ``hidden patterns'' for a particular problem and are relatively simple and easy to interpret.
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Ferro, Giulio, Riccardo Minciardi, Luca Parodi, Michela Robba, and Mansueto Rossi. "Optimal Control of Multiple Microgrids and Buildings by an Aggregator." Energies 13, no. 5 (February 27, 2020): 1058. http://dx.doi.org/10.3390/en13051058.
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The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.
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Lewis, Denice Colleen, Pierre Pluye, Charo Rodriguez, and Roland Grad. "Mining reflective continuing medical education data for family physician learning needs." Journal of Innovation in Health Informatics 23, no. 1 (April 6, 2016): 439. http://dx.doi.org/10.14236/jhi.v23i1.834.
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A mixed methods research (sequential explanatory design) studied the potential of mining the data from the consumers of continuing medical education (CME) programs, for the developers of CME programs. The quantitative data generated by family physicians, through applying the information assessment method to CME content, was presented to key informants from the CME planning community through a qualitative description study.The data were revealed to have many potential applications including supporting the creation of CME content, CME program planning and personal learning portfolios.
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AHN, JOONSEON, and TAISOOK HAN. "AN ANALYTICAL METHOD FOR PARALLELIZATION OF RECURSIVE FUNCTIONS." Parallel Processing Letters 10, no. 01 (March 2000): 87–98. http://dx.doi.org/10.1142/s012962640000010x.
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Programming with parallel skeletons is an attractive framework because it encourages programmers to develop efficient and portable parallel programs. However, extracting parallelism from sequential specifications and constructing efficient parallel programs using the skeletons are still difficult tasks. In this paper, we propose an analytical approach to transforming recursive functions on general recursive data structures into compositions of parallel skeletons. Using static slicing, we have defined a classification of subexpressions based on their data-parallelism. Then, skeleton-based parallel programs are generated from the classification. To extend the scope of parallelization, we have adopted more general parallel skeletons which do not require the associativity of argument functions. In this way, our analytical method can parallelize recursive functions with complex data flows.
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AHN, JOONSEON, and TAISOOK HAN. "AN ANALYTICAL METHOD FOR PARALLELIZATION OF RECURSIVE FUNCTIONS." Parallel Processing Letters 10, no. 04 (December 2000): 359–70. http://dx.doi.org/10.1142/s0129626400000330.
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Programming with parallel skeletons is an attractive framework because it encourages programmers to develop efficient and portable parallel programs. However, extracting parallelism from sequential specifications and constructing efficient parallel programs using the skeletons are still difficult tasks. In this paper, we propose an analytical approach to transforming recursive functions on general recursive data structures into compositions of parallel skeletons. Using static slicing, we have defined a classification of subexpressions based on their data-parallelism. Then, skeleton-based parallel programs are generated from the classification. To extend the scope of parallelization, we have adopted more general parallel skeletons which do not require the associativity of argument functions. In this way, our analytical method can parallelize recursive functions with complex data flows.
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Salloum, Alison, Erika A. Crawford, Adam B. Lewin, and Eric A. Storch. "Consumers’ and Providers’ Perceptions of Utilizing a Computer-Assisted Cognitive Behavioral Therapy for Childhood Anxiety." Behavioural and Cognitive Psychotherapy 43, no. 1 (July 25, 2013): 31–41. http://dx.doi.org/10.1017/s1352465813000647.
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Background: Computer-assisted cognitive behavioral therapy (CCBT) programs for childhood anxiety are being developed, although research about factors that contribute to implementation of CCBT in community mental health centers (CMHC) is limited. Aim: The purpose of this mixed-methods study was to explore consumers’ and providers’ perceptions of utilizing a CCBT for childhood anxiety in CMHC in an effort to identify factors that may impact implementation of CCBT in CMHC. Method: Focus groups and interviews occurred with 7 parents, 6 children, 3 therapists, 3 project coordinators and 3 administrators who had participated in CCBT for childhood anxiety. Surveys of treatment satisfaction and treatment barriers were administered to consumers. Results: Results suggest that both consumers and providers were highly receptive to participation in and implementation of CCBT in CMHC. Implementation themes included positive receptiveness, factors related to therapists, treatment components, applicability of treatment, treatment content, initial implementation challenges, resources, dedicated staff, support, outreach, opportunities with the CMHC, payment, and treatment availability. Conclusion: As studies continue to demonstrate the effectiveness of CCBT for childhood anxiety, research needs to continue to examine factors that contribute to the successful implementation of such treatments in CMHC.
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Pathre, Ayonija. "A Prefatory Analysis of Brain Computer Interfacing Based on EEG." ECS Transactions 107, no. 1 (April 24, 2022): 6789–99. http://dx.doi.org/10.1149/10701.6789ecst.
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An extremely developing area of application systems science is defined by brain programming interface technology. In health fields, its contributions range from treatment to synaptic healing for severe injuries. The special fingerprint of mind reading and remote contact in several areas, such as education, self-regulation, manufacturing, marketing, protection, entertainment, and games. It induces shared trust between consumers and systems around them. Deep learning has already received mainstream recognition and has been used in numerous applications, like natural language processing (NLP), computer vision, and voice. For MI EEG signal classification, however, deep learning has seldom been used. This paper highlights the fields of application that could advantage from brain waves in promoting or attaining their objectives. We also answer big usableness and technological problems facing the use of brain signals in different BCI device components. Various solutions aimed at minimizing and reducing their effects have also been studied. The popular spatial pattern (CSP) approach, which is generally utilized, is applied to extract variance-based CSP functions, that are then fed for classification to DNN. DNN practice has been thoroughly studied for classification of MI-BCI and best framework found has been explored.
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Ullah, Kalim, Sajjad Ali, Taimoor Ahmad Khan, Imran Khan, Sadaqat Jan, Ibrar Ali Shah, and Ghulam Hafeez. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs." Energies 13, no. 21 (November 2, 2020): 5718. http://dx.doi.org/10.3390/en13215718.
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An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.