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1
Abbott, Mary Ann, and Debby McBride. "AAC Decision-Making and Mobile Technology: Points to Ponder." Perspectives on Augmentative and Alternative Communication 23, no. 2 (April 2014): 104–11. http://dx.doi.org/10.1044/aac23.2.104.
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The purpose of this article is to outline a decision-making process and highlight which portions of the augmentative and alternative communication (AAC) evaluation process deserve special attention when deciding which features are required for a communication system in order to provide optimal benefit for the user. The clinician then will be able to use a feature-match approach as part of the decision-making process to determine whether mobile technology or a dedicated device is the best choice for communication. The term mobile technology will be used to describe off-the-shelf, commercially available, tablet-style devices like an iPhone®, iPod Touch®, iPad®, and Android® or Windows® tablet.
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Ulman, Kanchan, Mighfar Imam, Shobhana Narasimhan, Anders Odell, and Anna Delin. "Theoretical Study of Spin Conduction in the Ni/DTE/Ni Nanohybrid." Nano Hybrids 4 (May 2013): 1–20. http://dx.doi.org/10.4028/www.scientific.net/nh.4.1.
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The photoswitching molecule dithienylethene (DTE) is an interesting candidate for constructing optoelectronic molecular devices since it can be made to switch between a closed and an open conformation using light. We here report computations, based on density functional theory (DFT) and the non-equilibrium Green function (NEGF) method, of the spin-resolved conductance of the two DTE isomers attached to spin-polarized nickel leads. Results are compared and contrasted to those of other contact materials (nonmagnetic Ni, Ag, and Au), analyzing the physical origins of the various features in the transmission function. It was found rather surprisingly, that the two spin channels in the Ni/DTE/Ni device have almost identical I-V characteristics, despite one channel being d-dominated and the other one s-dominated. It was also observed that the Ni-based device exhibits a sustained high conductance ratio also for high bias - a property that may be of relevance in future device design. Furthermore, two computational schemes for calculating the conductance were compared and analyzed. It was found that even for very small bias the molecular orbital polarization was decisive for spin-related properties such as the spin current ratio and magneto-resistance in the Ni/DTE/Ni device.
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Stenina, Irina A., and Andrey B. Yaroslavtsev. "Ionic Mobility in Ion-Exchange Membranes." Membranes 11, no. 3 (March 11, 2021): 198. http://dx.doi.org/10.3390/membranes11030198.
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Membrane technologies are widely demanded in a number of modern industries. Ion-exchange membranes are one of the most widespread and demanded types of membranes. Their main task is the selective transfer of certain ions and prevention of transfer of other ions or molecules, and the most important characteristics are ionic conductivity and selectivity of transfer processes. Both parameters are determined by ionic and molecular mobility in membranes. To study this mobility, the main techniques used are nuclear magnetic resonance and impedance spectroscopy. In this comprehensive review, mechanisms of transfer processes in various ion-exchange membranes, including homogeneous, heterogeneous, and hybrid ones, are discussed. Correlations of structures of ion-exchange membranes and their hydration with ion transport mechanisms are also reviewed. The features of proton transfer, which plays a decisive role in the membrane used in fuel cells and electrolyzers, are highlighted. These devices largely determine development of hydrogen energy in the modern world. The features of ion transfer in heterogeneous and hybrid membranes with inorganic nanoparticles are also discussed.
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Elmouchi, Darryl A., Nagib Chalfoun, and Andre Gauri. "Attitudes of Implanting Physicians about Cardiac Rhythm Management Devices and Their Features." ISRN Cardiology 2013 (December 26, 2013): 1–6. http://dx.doi.org/10.1155/2013/247586.
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Modern cardiac rhythm management systems have become increasingly complex. The decision on which specific system to implant in a given patient often rests with the implanting physician. We conducted a multiple-choice survey to assess the opinions and preferences of cardiologists and electrophysiologists who implant and follow cardiac rhythm management systems. Reliability and battery longevity were viewed as the most important characteristics in device selection. Patient characteristics which most affected device choice were pacing indication and life expectancy. Remote technology was used in 47% of pacemaker patients, 64% of ICD patients, and 65% of CRT-D patients, with wireless (radiofrequency) remote patient monitoring associated with higher patient compliance rates (74% versus 64%, resp.). Wireless remote patient management with alerts for atrial tachyarrhythmias was felt to be important by 76% of respondents. When choosing an MR-conditional device, physicians deemed patients with prior orthopedic problems, a history of cancer, or neurological disorders to be more likely to require a future MRI. Device longevity and reliability remain the most important factors which influence device selection. Wireless remote patient monitoring with alerts is considered increasingly important when choosing a specific cardiac rhythm management system to implant.
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Tundis, Andrea, Ali Faizan, and Max Mühlhäuser. "A Feature-Based Model for the Identification of Electrical Devices in Smart Environments." Sensors 19, no. 11 (June 8, 2019): 2611. http://dx.doi.org/10.3390/s19112611.
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Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided.
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Afolabi, Bosede A., and Fred M. Kusumoto. "Remote Monitoring of Patients with Implanted Cardiac Devices – A Review." European Cardiology Review 8, no. 2 (2012): 88. http://dx.doi.org/10.15420/ecr.2012.8.2.88.
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There has been a rapid growth in the number of patients with cardiovascular implantable electronic devices (CIEDs), due to the consistent good results from large randomised trials and changing worldwide demographics with progressive ageing in all developed countries. Early generations of CIEDs provided only basic operations and stored only rudimentary data, but the evolution of all types of CIEDs (pacemakers, defibrillators, cardiac resynchronisation devices, implantable monitors) has led to their increased complexity and the development of a myriad of specialised features. As an outgrowth of this increased sophistication, once implanted, CIEDs can provide significant amounts of important clinical information, allowing to identify the presence of significant arrhythmias, assess drug efficacy, evaluate heart failure status and continuously monitor device function. With the advent of new methods of remote monitoring, the information recorded by these devices can be accessible in real time and thus lead to more timely clinical decision-making. This article summarises the impact of remote monitoring on clinical practice today and how the use of remote monitoring may evolve to affect the practice of medicine in the future.
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Marques, Célio Gonçalo Cardoso, António Manso, Ana Paula Ferreira, and Felisbela Morgado. "Using Mobile Technologies in Education." International Journal of Technology and Human Interaction 13, no. 4 (October 2017): 77–90. http://dx.doi.org/10.4018/ijthi.2017100106.
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The acquisition of reading skills is decisive for the academic achievement of students. However, learning to read is a complex process. With this in mind, several attempts have been made to find new educational approaches to enhance students' reading motivation. Considering the enormous potential of ICT for education and training, we have developed a digital repository of teaching and learning materials and a multiplatform application that runs on mobile devices: Letrinhas. This information system was designed to promote the development of reading and to provide tools for monitoring and assessing reading skills against the curricular targets set by the Ministry of Education. Letrinhas was evaluated by specialists and users and a high level of satisfaction was observed among students and teachers as time and effort spent to consolidate reading is considerably reduced with this application. This evaluation also enabled to identify features that will be available in the future.
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Rawindaran, Nisha, Ambikesh Jayal, Edmond Prakash, and Chaminda Hewage. "Cost Benefits of Using Machine Learning Features in NIDS for Cyber Security in UK Small Medium Enterprises (SME)." Future Internet 13, no. 8 (July 21, 2021): 186. http://dx.doi.org/10.3390/fi13080186.
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Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment; however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs.
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Adiguzel, Yekbun, Kristel P. Ramirez Valdez, and Gulkizilca Yurur. "Medical Use of Sensor-Based Devices, the Debates Around and Implementation in Education." Reports in Advances of Physical Sciences 02, no. 01 (March 2018): 1850001. http://dx.doi.org/10.1142/s2424942418500019.
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Sensor-based diagnostics are increasing rapidly and in clinics, they can transform the health care as they will be in use out of clinics as well, namely, by the non-clinicians and people without expertise. The trade-off between the advantages and disadvantages of their implementation into the clinical settings should be decisive in their use, at the current state. Yet, disadvantages must be carefully worked out and tried to be eliminated in any case, while keeping the inborn benefits. Therefore, we would like to draw attention to the reliability and security risks of personal health data and associated concerns. We further discuss the related issues of sensor-based diagnostics, mobile health (mHealth) and eHealth. The debate starts with the current states of the rules and regulations. It is argued that there is prompt need for internationally consolidated solutions for vast device types and uses onto which the local needs may have to be implemented without violating the basic assets such as the inherent privacy rights of the users/patients. The resistance factors against the sensor-based healthcare devices and applications are also conferred. There are additionally data quality and assessment issues, and in relation to the data assessment, concerns that are associated with the psychological responses of the layman to the health data are mentioned. For these and more reasons, and finally for proper use and implementation of sensor-based tests and devices in the clinical settings, education of both professionals and non-professionals seems to be the key. All these require much work and maybe even more workforces to be allocated for the emerging, associated tasks. However, there are economic benefits, and beyond those, they bring new features in the health care that were deemed to be impossible. Besides, despite the apparent unethical use risks, they can result in better ethical practices, e.g., possible prevention of unnecessary tests on animals when similar test on organ-on-chips would be failing.
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Shutova, N. M. "STYLISTICS OF ADVERTISING TEXT AS A TRANSLATION TASK (ON CAR ADVERTISING MATERIAL)." Bulletin of Udmurt University. Series History and Philology 29, no. 3 (June 25, 2019): 461–70. http://dx.doi.org/10.35634/2412-9534-2019-29-3-461-470.
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The paper concerns stylistic parameters of car advertisements in English and the problems of their preservation in Russian translations. Although commercials today quite actively employ non-verbal expressive means, the verbal part in most cases plays a decisive role in the effectiveness of advertising. The choice of car advertisements for the present study was predetermined by extreme popularity of the car market and its special inventiveness in the art of advertising. The paper’s objective was to define the main stylistic features of car advertisements and slogans - short and striking phrases usually preceding the advertising text itself. Another objective was to single out the most popular stylistic devices and discuss the possible ways of their translation into Russian. The conducted research makes it possible to conclude that epithets, parallel constructions and metaphors are most frequently used to create certain symbolic images. Linguostylistic, lexicographic, situational and comparative analyses were applied to the data processing. The cited advertisements were taken from the websites of the corresponding producers of cars.
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Chen, Wen, Wu, Xu, Jiang, Song, and Chen. "Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication." Sensors 19, no. 16 (August 19, 2019): 3610. http://dx.doi.org/10.3390/s19163610.
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In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning algorithms for recognition are performed by the remote cloud. By this means, the authentication rate can be improved by taking advantage of the machine-learning training methods and computing resource support of the cloud. Extensive simulations are performed under the IoT application scenario. The results show that the novel method can achieve higher recognition rate than that of traditional RFFID method by using wavelet feature effectively, which demonstrates the efficiency of our proposed method.
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Zhang, Haichun, Jie Wang, Zhuo Chen, Yuqian Pan, Zhaojun Lu, and Zhenglin Liu. "An SVM-Based NAND Flash Endurance Prediction Method." Micromachines 12, no. 7 (June 25, 2021): 746. http://dx.doi.org/10.3390/mi12070746.
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NAND flash memory is widely used in communications, commercial servers, and cloud storage devices with a series of advantages such as high density, low cost, high speed, anti-magnetic, and anti-vibration. However, the reliability is increasingly getting worse while process improvements and technological advancements have brought higher storage densities to NAND flash memory. The degradation of reliability not only reduces the lifetime of the NAND flash memory but also causes the devices to be replaced prematurely based on the nominal value far below the minimum actual value, resulting in a great waste of lifetime. Using machine learning algorithms to accurately predict endurance levels can optimize wear-leveling strategies and warn bad memory blocks, which is of great significance for effectively extending the lifetime of NAND flash memory devices and avoiding serious losses caused by sudden failures. In this work, a multi-class endurance prediction scheme based on the SVM algorithm is proposed, which can predict the remaining P-E cycle level and the raw bit error level after various P-E cycles. Feature analysis based on endurance data is used to determine the basic elements of the model. Based on the error features, we present a variety of targeted optimization strategies, such as extracting the numerical features closely related to the endurance, and reducing the noise interference of transient faults through short-term repeated operations. Besides a high-parallel flash test platform supporting multiple protocols, a feature preprocessing module is constructed based on the ZYNQ-7030 chip. The pipelined module of SVM decision model can complete a single prediction within 37 us.
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Guryanov, A. V. "RESEARCH OF ORGANIZATION OF SUPPLY CHAIN MANAGEMENT TO MAINTAIN THE FUNCTIONALITY OF AVIONIC EQUIPMENT." System analysis and logistics 4, no. 26 (December 17, 2020): 26–34. http://dx.doi.org/10.31799/2007-5687-2020-4-26-34.
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The article reveals the issues of replacing spare serviceable devices (spare parts) for avionics objects for aircraft, discusses the organization and construction of supply chains, discusses the features of the functioning of avionics objects and their repair. The necessity of including blocks for modeling various scenarios of supply chains into the decision-making loop for the assembly and launch of spare devices is substantiated. Particular attention is paid to the classification of methods for calculating spare parts, based on the theory of restoration processes. The graphs of the function of increasing the functioning of avionics objects, graphs of the reliability of objects and graphs of forecasting the number of deliveries are given. The article discusses the possibilities of organizing supplies and their unique features. The necessity of modeling various delivery options and the limited data obtained on the basis of standard parameters, which include the technical reliability of the parameters of specific devices, are proved. Key words: SPTA, avionics, instrument, supply chain, aviation instrument reliability.
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Lapegna, Marco, Walter Balzano, Norbert Meyer, and Diego Romano. "Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments." Sensors 21, no. 16 (August 10, 2021): 5395. http://dx.doi.org/10.3390/s21165395.
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The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.
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Afrooz, Sonia, and Nima Jafari Navimipour. "Memory Designing Using Quantum-Dot Cellular Automata: Systematic Literature Review, Classification and Current Trends." Journal of Circuits, Systems and Computers 26, no. 12 (August 2017): 1730004. http://dx.doi.org/10.1142/s0218126617300045.
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Quantum-dot cellular automata (QCA) has come out as one of the potential computational structures for the emerging nanocomputing systems. It has a large capacity in the development of circuits with high space density and dissipation of low heat and allows faster computers to develop with lower power consumption. The QCA is a new appliance to realize nanolevel digital devices and study and analyze their various parameters. It is also a potential technology for low force and high-density memory plans. Large memory designs in QCA show unique features because of their architectural structure. In QCA-based architectures, memory must be maintained in motion, i.e., the memory state has to be continuously moved through a set of QCA cells. These architectures have different features, such as the number of bits stored in a loop, access type (serial or parallel) and cell arrangement for the memory bank. However, the decisive features of the QCA memory cell design are the number of cells, to put off the use of energy. Although the review and study of the QCA-based memories are very important, there is no complete and systematic literature review about the systematical analyses of the state of the mechanisms in this field. Therefore, there are five main types to provide systematic reviews about the QCA-based memories; including read only memory (ROM), register, flip-flop, content addressable memory (CAM) and random access memory (RAM). Also, it has provided the advantages and disadvantages of the reviewed mechanisms and their important challenges so that some interesting lines for any coming research are provided.
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Zong Chen, Joy Iong, and Kong-Long Lai. "Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study." December 2020 2, no. 4 (January 19, 2021): 236–45. http://dx.doi.org/10.36548/jscp.2020.4.005.
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In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.
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Cho, Soo Sun, Dong Won Han, and Chi Jung Hwang. "Web Image Classification Using an Optimized Feature Set." Key Engineering Materials 277-279 (January 2005): 361–68. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.361.
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Redundant images currently abundant in World Wide Web pages need to be removed in order to transform or simplify the Web pages for suitable display in small-screened devices. Classifying removable images on the Web pages according to their uniqueness of content will allow simpler representation of Web pages. For such classification, machine learning based methods can be used to categorize images into two groups; eliminable and non-eliminable. We use two representative learning methods, the Naïve Bayesian classifier and C4.5 decision trees. For our Web image classification, we propose new features that have expressive power for Web images to be classified. We apply image samples to the two classifiers and analyze the results. In addition, we propose an algorithm to construct an optimized subset from a whole feature set, which includes most influential features for the purposes of classification. By using the optimized feature set, the accuracy of classification is found to improve markedly.
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Wang, Ying, Shaopeng Lin, Xiaoyue Zhang, Weiming Xiong, Biao Wang, and Yue Zheng. "Reliable resistive switching and its tunability in La-doped PbTiO3\TiO2 composite bilayer." Functional Materials Letters 08, no. 04 (August 2015): 1550033. http://dx.doi.org/10.1142/s1793604715500332.
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Nanoscale La-doped PbTiO 3( PLT )\ TiO 2 (PLTT) composite structures have been fabricated. It shows that the structure presents reliable resistive switching (RS) behavior, and importantly, has great tunability on RS characteristics such as forming/set/reset voltages and resistance ratio by adjusting the PLT layer thickness. Particularly, the set voltage can be tuned at a large range from several volts to dozens of volts. Meanwhile, the set current keeps almost the same, indicating the RS is current dominating. The space-charge-limited current (SCLC) feature indicates that the localized traps are decisive for the RS. Our result sheds light on the prospects of composite structures for designing tunable RS devices.
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Bogach, V. M., J. M. Dovidenko, and I. M. Slobodianiuk. "Features of systems greasing of diesel engines MANB&W." Ship power plant 1 (August 5, 2020): 144–51. http://dx.doi.org/10.31653/smf340.2020.144-151.
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The abstract The publication is devoted the decision of an actual problem increase efficiency operation of ship diesel engines by perfection processes greasing of cylinders. The analysis a condition of a question on an investigated problem is made and lacks systems greasing of diesel engines MAN-B&W are defined. Experimental researches on studying processes greasing of cylinders of ship engines that has allowed to receive representation about an overall performance these systems are spent. Modern methods researches, such as oscillograms and high-speed filming are thus used. Characteristics process greasing, and their interrelation with a design of greasing devices are defined. Influence geometrical parametres of channels system greasing on characteristics process the expiration of oil in the cylinder and finally on efficiency its use in the engine is experimentally confirmed. Keywords: ship diesel engine, greasing system, greasing channel, greasing process, cylinder, piston, a piston ring.
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Szpytko, Janusz. "Human Reliability Model." Journal of Konbin 8, no. 1 (January 1, 2008): 189–200. http://dx.doi.org/10.2478/v10040-008-0112-9.
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Human Reliability ModelThe paper is focusing on reliability model of transport devices' human operator. The presented operator model is base on operation potential approach, with taken into account his features and states helping assure of safety decision-making process. The human reliability model is important for future improvement the human - machine interfaces (HMI).
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Widarma, Adi, M. Dedi Irawan, Fajri Nurhidayahti, and Ranis Hsb. "Decision Support System Determining Computer Virus Protection Applications Using Simple Additive Weighting (SAW) Method." Journal of Computer Networks, Architecture, and High-Performance Computing 3, no. 1 (March 2, 2021): 86–79. http://dx.doi.org/10.47709/cnahpc.v3i1.936.
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The use of information technology devices such as computers or laptops is currently increasing. The increased use is due to the fact that these devices are very supportive of our daily work activities. With the increasing use of these computers, data security on a computer or laptop device must be completely safe from virus attacks. To ward off viral attacks m aka requires the application of anti-virus to inhibit and prevent a variety of viruses that enter into the computer system so that the computer user's activity was not bothered by the many viruses are easily spread. Because there are too many antiviruses on the market, it is necessary to choose a good antivirus. One of the ways to choose antivirus is the existence of a decision support system . In this study, the Simple Additive Weighting (SAW) method was applied for the anti-virus application selection system. This data assessment analysis aims to produce the best anti - virus application options that computer users can use to secure their computer data. The criteria and weights used are K1 = application rating (5%) , K2 = completeness of features (30%) , K3 = price / official license (5%) , K4 = malware detection (45%) and K5 = blocking URL (15%). Of the 25 alternatives used, the results of the study, namely alternative A1 = Kaspersky anti-virus get the highest ranking result.
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Barbareschi, Mario, Salvatore Barone, and Nicola Mazzocca. "Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study." Knowledge and Information Systems 63, no. 6 (April 12, 2021): 1577–96. http://dx.doi.org/10.1007/s10115-021-01565-5.
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AbstractSo far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit approximate computing design paradigm to trade hardware area overhead off for classification accuracy. In particular, starting from trained DT models and employing precision-scaling technique, we explore approximate decision tree variants by means of multiple objective optimization problem, demonstrating a significant performance improvement targeting field-programmable gate array devices.
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Vaijayanthimala, J., and T. Padma. "Synthesis Score Level Fusion Based Multifarious Classifier for Multi-Biometrics Applications." Journal of Medical Imaging and Health Informatics 9, no. 8 (October 1, 2019): 1673–80. http://dx.doi.org/10.1166/jmihi.2019.2762.
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In this paper, we are presenting a face and signature recognition method from a large dataset with the different pose and multiple features. Initially, Face and corresponding signature are detected from devices for further pre-processing. Face recognition is the first stage of a system then the signature verification will be done. The proposed Legion feature based verification method will be developed using four important steps like, (i) feature extraction from face and data glove signals using feature Extraction. The various Features like Local binary pattern, shape and geometrical features of face, then the global and local features of the signatures were extracted. (ii) Score match normalization is used to enhance the recognition accuracy using min–max and median estimations. (iii) Then the match scores are evaluated using synthesis score level fusion based feature matching through Euclidean distance, (iv) Recognition based on the final score. Finally based on the feature library the face image and signature can be recognized. The similarity measurement is done by using Synthesis score level fusion (SSF) based multifarious Neural network (MNN) Classifier with weighted summation formulae where two weights will be optimally found out using Adapted motion search optimization algorithm. Finally SSF-MNN based matching score fusion based decision classifier to determine recognized and non-recognized biometrics. Moreover, in comparative analysis, a proposed technique is compared with the existing method by several performance metrics and the proposed SSF-MNN technique efficiently recognize the face images and corresponding signature from the input databases than the existing technique.
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Alkaaf, Howida Abuabker, Aida Ali, Siti Mariyam Shamsuddin, and Shafaatunnur Hassan. "Exploring permissions in android applications using ensemble-based extra tree feature selection." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 543. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp543-552.
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<span>The fast development of mobile apps and its usage has led to increase the risk of exploiting user privacy. One method used in Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, that permissions could be exploited by attackers when granting certain combinations of permissions. So, the aim of this paper is to explore the pattern of malware apps based on analyzing permissions by proposing framework utilizing feature selection based on ensemble extra tree classifier method and machine learning classifier. The used dataset had 25458 samples (8643 malware apps & 16815 benign apps) with 173 features. Three dataset with 25458 samples and 5, 10 and 20 features respectively were generated after using the proposed feature selection method. All the dataset was fed to machine learning. Support Vector machine (SVM), K Neighbors Classifier, Decision Tree, Naïve bayes and Multilayer Perceptron (MLP) classifiers were used. The classifiers models were evaluated using true negative rate (TNR), false positive rate (FNR) and accuracy metrics. The experimental results obtained showed that Support Vector machine and KNeighbors Classifiers with 20 features achieved the highest accuracy with 94 % and TNR with rate of 89 % using KNeighbors Classifier. The FNR rate is dropped to 0.001 using 5 features with support vector machine (SVM) and Multilayer Perceptrons (MLP) classifiers. The result indicated that reducing permission features improved the performance of classification and reduced the computational overhead.</span>
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Abdulwahab, Samaa, Hussain Khleaf, and Manal Jassim. "EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (July 30, 2021): 38–45. http://dx.doi.org/10.37917/ijeee.17.2.5.
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The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.
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Dolovich, Myrna B., and Jolyon P. Mitchell. "Canadian Standards Association Standard CAN/CSA/Z264.1-02:2002: A New Voluntary Standard for Spacers and Holding Chambers Used with Pressurized Metered-Dose Inhalers." Canadian Respiratory Journal 11, no. 7 (2004): 489–95. http://dx.doi.org/10.1155/2004/497946.
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A new Canadian standard (CAN/CSA/Z264.1-02:2002) has been published with the purpose of helping to ensure the safety, efficacy and functionality of spacers and/or holding chambers. They are prescribed for use by spontaneously breathing patients for the treatment of various respiratory diseases where medication is delivered to the lungs using pressurized-metered dose inhalers. This consensus standard was developed with the support of pharmaceutical companies and manufacturers of spacers and holding chambers, and with the help of clinicians, retail pharmacists and representatives of patient advocate bodies associated with respiratory diseases and the dissemination of information related to the treatment and the delivery of inhaled medications. Advice was also sought from expert groups outside of Canada to ensure that the standard would be relevant internationally. Whereas monographs in the pharmaceutical compendia and guidance documents published by regulatory bodies provide information that is largely about the drug product and inhaler, this is the only standard whose focus is primarily on these add-on devices. The purpose of the present review is to highlight the main features of the standard for clinicians by describing its scope, the tests that are intended to assure the robustness of the construction of these devices, the type of testing that is specified to establish in vitro efficacy, and the recommendations for the marking and labelling of the device and its associated packaging. Manufacturers who test their products to this Canadian Standards Association standard will be able to provide performance information about add-on devices to the clinician, facilitating an informed decision when selecting devices for patients.
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Abdali-Mohammadi, Fardin, Maytham N. Meqdad, and Seifedine Kadry. "Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 766. http://dx.doi.org/10.11591/ijai.v9.i4.pp766-771.
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Internet of Things (IoT) refers to the practice of designing and modeling objects connected to the Internet through computer networks. In the past few years, IoT-based health care programs have provided multidimensional features and services in real time. These programs provide hospitalization for millions of people to receive regular health updates for a healthier life. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. In this paper, a disease diagnosis system is designed based on the Internet of Things. In this system, first, the patient's courtesy signals are recorded by wearable sensors. These signals are then transmitted to a server in the network environment. This article also presents a new hybrid decision making approach for diagnosis. In this method, a feature set of patient signals is initially created. Then these features go unnoticed on the basis of a learning model. A diagnosis is then performed using a neural fuzzy model. In order to evaluate this system, a specific diagnosis of a specific disease, such as a diagnosis of a patient's normal and unnatural pulse, or the diagnosis of diabetic problems, will be simulated.
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Hemmatpour, Masoud, Renato Ferrero, Bartolomeo Montrucchio, and Maurizio Rebaudengo. "A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results." Advances in Human-Computer Interaction 2019 (July 1, 2019): 1–12. http://dx.doi.org/10.1155/2019/9610567.
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Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance.
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Basel, Almourad, John McAlaney, Tiffany Skinner, Megan Pleva, and Raian Ali. "Defining digital addiction: Key features from the literature." Psihologija 53, no. 3 (2020): 237–53. http://dx.doi.org/10.2298/psi191029017a.
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Within recent years there has been increasing societal concern around the compulsive and excessive use of digital and Internet-enabled devices, such as the use of social media or online video gaming, and associated psychological and physical harms. However, problematic use or addictive behaviours are not yet included as diagnosable mental health issues in any major diagnostic system in Western countries and the conceptualisations of the phenomena are still inconsistent. To address this issue, the present study reviewed the current conceptualisations of digital addiction used within the research literature and identified common features of the definition of digital addiction. Definitions of the phenomenon were extracted from 47 studies, and they were analysed using a content analysis approach. The initial process assessed definitions for features of digital addiction within Internet, gaming and smartphone addiction. Two higher-order themes were identified, which focused on the harm caused by the phenomenon and on the user?s behaviours associated with the phenomenon. It was also found that key constructs are not specific to the usage domain, i.e. whether it is related to gaming, Internet or smartphone use. Several core features were found across different conceptualisations of digital addiction within the literature; however, it was also noted that some features are subjective and inconsistently applied. If a decision is to be reached on whether the phenomenon is a mental health disorder, then clearer definitions must be created.
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Faeni, You Ari. "A Survey of Models, Technologies and Trends in Decision Making with Internet of Things." ITEJ (Information Technology Engineering Journals) 4, no. 1 (July 31, 2019): 26–38. http://dx.doi.org/10.24235/itej.v4i1.48.
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The development of IoT (internet of things) technology encourages increased use of smart devices and their supporting applications. The use of smart devices and applications that are growing rapidly and large will certainly produce large amounts of data and information. The next challenge is how to use the data and information generated to help people solve various problems that exist and improve the quality of human life. Data is analyzed into information, knowledge and in the end it will be used to make decisions. With all the features offered by IoT, decision making is expected to be easier and more precise, according to the expected goals. This paper will discuss the IoT architecture and decision-making framework with IoT. Furthermore, it provides an overview of the models, technology and development of the decision making process with IoT based on previously published papers (journals and conferences). The main purpose of making this paper is to provide insights for researchers about decision making with IoT.
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Wilson, Leah M., Nichole Tyler, Peter G. Jacobs, Virginia Gabo, Brian Senf, Ravi Reddy, and Jessica R. Castle. "Patient Input for Design of a Decision Support Smartphone Application for Type 1 Diabetes." Journal of Diabetes Science and Technology 14, no. 6 (August 23, 2019): 1081–87. http://dx.doi.org/10.1177/1932296819870231.
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Background: Decision support smartphone applications integrated with continuous glucose monitors may improve glycemic control in type 1 diabetes (T1D). We conducted a survey to understand trends and needs of potential users to inform the design of decision support technology. Methods: A 70-question survey was distributed October 2017 through May 2018 to adults aged 18-80 with T1D from a specialty clinic and T1D Exchange online health community ( myglu.org ). The survey responses were used to evaluate potential features of a diabetes decision support tool by Likert scale and open responses. Results: There were 1542 responses (mean age 46.1 years [SD 15.2], mean duration of diabetes 26.5 years [SD 15.8]). The majority (84.2%) have never used an app to manage diabetes; however, a large majority (77.8%) expressed interest in using a decision support app. The ability to predict and avoid hypoglycemia was the most important feature identified by a majority of the respondents, with 91% of respondents indicating the highest level of interest in these features. The task that respondents find most difficult was management of glucose during exercise (only 47% of participants were confident in glucose management during exercise). The respondents also highly desired features that help manage glucose during exercise (85% of respondents were interested). The responses identified integration and interoperability with peripheral devices/apps and customization of alerts as important. Responses from participants were generally consistent across stratified categories. Conclusions: These results provide valuable insight into patient needs in decision support applications for management of T1D.
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EKÁRT, ANIKÓ, and ANDRÁS MÁRKUS. "Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 3 (August 2003): 205–20. http://dx.doi.org/10.1017/s0890060403173040.
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Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.
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PINARBAÅžI, Fatih. "DEMYSTIFYING MUSICAL PREFERENCES AT TURKISH MUSIC MARKET THROUGH AUDIO FEATURES OF SPOTIFY CHARTS." TURKISH JOURNAL OF MARKETING 4, no. 3 (December 25, 2019): 264–79. http://dx.doi.org/10.30685/tujom.v4i3.62.
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Online music streaming services are one of the important actors in music consumption for today’s consumers. In addition to widespread use of mobile devices, many changes in the patterns of music consumption are witnessed such as the purchase of single tracks instead of albums, listening to music on different platforms, and personalized music consumption options. This study aims to examine the concept of music consumption in Turkey through audio characteristics of popular songs. Top 200 popular song-lists for 6 months period are chosen as sample and audio characteristics provided by Spotify API service regarding 676 unique songs are analyzed. Following descriptive statistics of Turkey Music Market, clustering methodology is employed and three different clusters for songs are concluded. Finally, decision tree methodology is employed to classify the dataset with popularity scores and audio characteristics together, while loudness and energy characteristics are found as significant classifiers.
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Tannen, Robert. "Parallel User Interface Design of a Clinical Decision-Support Application for Desktop and Pocket Pc Platforms." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 16 (September 2002): 1423–27. http://dx.doi.org/10.1177/154193120204601604.
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In order to increase physician acceptance and use, it is necessary for clinical information systems to better support workflow and connectivity. Towards that end, it is advantageous to develop clinical applications that support a range of platforms and mobile devices. However, differences in design/development approaches, technical limitations, and user interactivity across devices result in inconsistent features and user experiences, limiting functionality, usability, and transfer of training. In the current project, a browser-based physician decision-support and order entry prototype was developed for the Windows desktop and Pocket PC in parallel. Corresponding functionality was implemented on both platforms via an iterative, user-centered design approach that utilized components of the desktop version to create the PocketPC screens. Subsequent physician feedback demonstrated high transfer of training from the desktop version to the PocketPC. The findings from this work can be applied to other multi-platform user interface projects.
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Chen, Yeou-Jiunn, Pei-Chung Chen, Shih-Chung Chen, and Chung-Min Wu. "Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs." Sensors 21, no. 15 (July 23, 2021): 5019. http://dx.doi.org/10.3390/s21155019.
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For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.
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Xia, Linyuan, Qiumei Huang, and Dongjin Wu. "Decision Tree-Based Contextual Location Prediction from Mobile Device Logs." Mobile Information Systems 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1852861.
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Contextual location prediction is an important topic in the field of personalized location recommendation in LBS (location-based services). With the advancement of mobile positioning techniques and various sensors embedded in smartphones, it is convenient to obtain massive human mobile trajectories and to derive a large amount of valuable information from geospatial big data. Extracting and recognizing personally interesting places and predicting next semantic location become a research hot spot in LBS. In this paper, we proposed an approach to predict next personally semantic place with historical visiting patterns derived from mobile device logs. To address the problems of location imprecision and lack of semantic information, a modified trip-identify method is employed to extract key visit points from GPS trajectories to a more accurate extent while semantic information are added through stay point detection and semantic places recognition. At last, a decision tree model is adopted to explore the spatial, temporal, and sequential features in contextual location prediction. To validate the effectiveness of our approach, experiments were conducted based on a trajectory collection in Guangzhou downtown area. The results verified the feasibility of our approach on contextual location prediction from continuous mobile devices logs.
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Stojanovic, V., M. Trapp, R. Richter, and J. Döllner. "A SERVICE-ORIENTED INDOOR POINT CLOUD PROCESSING PIPELINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 339–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-339-2019.
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Abstract. Visualization of point clouds plays an important role in understanding the context of the digital representation of the built environment. Modern commodity mobile devices (e.g., smartphones and tablets), are capable of capturing representations in the form of 3D point clouds, with their depth-sensing and photogrammetry capabilities. Points clouds enable the encoding of important spatial and physical features of the built environment they represent. However, once captured, point clouds need to be processed before they can be used for further semantic enrichment and decision making. An integrated pipeline for such processes is crucial for use in larger and more complex enterprise systems and data analysis platforms, especially within the realm of Facility Management (FM) and Real Estate 4.0. We present and discuss a prototypical implementation for a service-oriented point cloud processing pipeline. The presented processing features focus on detecting and visualizing spatial deviations between as-is versus as-designed representations. We discuss the design and implementation of these processing features, and present experimental results. The presented approach can be used as a lightweight software component for processing indoor point clouds captured using commodity mobile devices, as well as primary deviation analysis, and also provides a processing link for further semantic enrichment of base-data for Building Information Modeling (BIM) and Digital Twin (DT) applications.
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CHEN, KEH-JIANN, KUO-CHUN LI, and YEONG-LONG CHANG. "A SYSTEM FOR ON-LINE RECOGNITION OF CHINESE CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 01 (March 1988): 139–48. http://dx.doi.org/10.1142/s0218001488000108.
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The target of this recognition system is the set of handwritten Chinese characters input from tablet devices with stroke-sequence and stroke-count being free but within the constraint of normal writing. A formalism based upon an initial stroke-sequence decision tree and position matching has been developed for recognizing handwritten Chinese characters. This formalism has the advantages of using the features of strokes, stroke-sequence, and geometric relations but avoids the disadvantages caused by the instability of all of the above features. With extensive training, it can be proven that this formalism may provide a very promising result even in handling erroneous writing such as missing a stroke, wrong writing sequence etc.
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Vishniakou, U. A., and B. H. Shaya. "Modeling the Internet of Things network for monitoring audio information on the Amazon platform." «System analysis and applied information science», no. 2 (August 19, 2021): 28–33. http://dx.doi.org/10.21122/2309-4923-2021-2-28-33.
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The subject of research is modeling the structure of the Internet of things (IoT) network for controlling audio information based on the IoT platform. The purpose of the article is to detail the process of modeling the audio information monitoring network based on the IoT platform. The authors proposed the structure of a multi-agent system (MAS) for monitoring audio information (MASAI). The structure of MASAI includes many agents of sound transformation, analysis of information received from them, and decision-making. It was decided to use the IoT network, which includes sound sensors, the IoT platform, the notification service, and the user’s application to simulate the MASAI. The structure of this network using the Amazon platform is proposed. An algorithm for modeling the Internet of things network for analyzing audio information based on the AWS platform is presented, including simulating audio sensors, transmitting this information to the platform, sensors authenticating, processing information according to certain rules, generating notifications to a user. Detailed structure of the AWS platform is provided with a description of the functions of its components such as: device gateway, rule machine, certificate block, device copy block, database, analytics block, notification service. The algorithm for connecting devices to the AWS platform is given: creating a device certificate on the platform, creating a security policy, rules for processing information received from devices, and testing the network. The features of the algorithm for modeling the readings of sound information sensors on a smartphone are shown, steps are given for organizing its communication with the platform, performing security procedures, sending data in the form of an MQTT message, and displaying the captured audio information.
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Tyagi, Himani, and Rajendra Kumar. "Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches." Revue d'Intelligence Artificielle 35, no. 1 (February 28, 2021): 11–21. http://dx.doi.org/10.18280/ria.350102.
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IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.
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Cardoso, Patricia, Dianne V. Hawk, and Donna Cross. "What Young People Need to Make Better-Informed Decisions When Communicating With Digital Images: Implications for Mental Health and Well-Being." Health Education & Behavior 47, no. 1 (November 14, 2019): 29–36. http://dx.doi.org/10.1177/1090198119885433.
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Sixty-eight young people contributed to a Design Thinking Challenge created to elicit a better understanding of their electronic image-sharing experiences, the helpful and harmful consequences of image-sharing to adolescent mental health and safety, and promising interventions that allow young people to make more positive decisions and minimize their risks when sharing images through electronic devices. Through this collaborative group-based process, each co-design group engaged in a four-phase process to discover, define, develop, and deliver an intervention that took the form of a paper-based mobile app prototype. Young people reported that they need information and advice to support their and others’ online decision making, help making situational decision-making skills for managing online interactions, and means to control information and images that can be accessed and distributed. Detailed app features that they required to address their decision-making needs are also discussed. These app intervention features highlight what young people need to make better-informed decisions when communicating through images electronically.
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Shen, Xuehao, Yuezhong Wu, Shuhong Chen, and Xueming Luo. "An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles." Mobile Information Systems 2021 (August 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/5231092.
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In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.
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Walsh, Kieran. "Infectious disease outbreaks: how online clinical decision support could help." BMJ Simulation and Technology Enhanced Learning 5, no. 4 (July 21, 2018): 218–20. http://dx.doi.org/10.1136/bmjstel-2018-000368.
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This paper describes an evaluation of how doctors might use an online clinical decision support tool to improve the care that they would provide to patients with infectious disease and what features they would expect in such a clinical decision support tool. Semistructured interviews were conducted by telephone with doctors to evaluate the utility of a clinical decision support tool in helping them to improve the care that they would provide to patients with infectious disease and to assess the features that they would value in such a tool. The doctors were primarily interested in how they could use the tool to improve care. They were short of time and so needed to be able to access the content that they needed really quickly. They expected content that was both evidence based and current, and they used a range of devices to access the content. They used desktops, laptops, mobiles and sometimes mobile apps. Doctors view the utility of clinical decision support in the management of rare infectious diseases from a number of perspectives. However, they primarily see utility in the tools as a result of their capacity to improve clinical practice in infectious diseases.
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Et. al., Annamalai M,. "Smart IOT Based Healthcare Monitoring and Decision-Making System Using Augmented Data Recognition Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (May 10, 2021): 1971–79. http://dx.doi.org/10.17762/turcomat.v12i11.6153.
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Many medical errors occur because people are in charge of patients or elderly medications by handling large amounts of medications every day. This work consists of designing and establishing a pillbox prototype intended to address this shortcoming in medical areas. It can be used separately from the medication itself and other advanced features provided with this device by the hospital or retirement home. This medication pack aims to take most medications or vitamin supplements or stimulants that deal with over-salting or over-the-counter patients. The proposed smart pillbox contains a program that enables medical caregivers or clients to determine the pill size and timing of pills and service routine each day. In this research work, the Augmented Data Recognition (ADR) algorithm is also used to monitor humans' health conditions. Initially, the UCI dataset is used for training and validation of the proposed ADR algorithm. The heart rate, blood pressure and temperature of the patient have carried during the testing phase via the Internet of Things (IoT) setup. The testing phase estimates any abnormalities in the health status based on the information obtained by the sensor collected by the population structure. Statistical analysis is based on data obtained from a cumulative cloud from IoT devices to estimate percentage accuracy.
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Akilin, Gleb A., Vladimir А. Fedorov, Evgeny V. Gritskevich, and Polina A. Zviagintseva. "COORDINATOR SIMULATION COMPUTER MODEL WORKING AS APART OF BIOMETRIC RECOGNITION SYSTEM." Interexpo GEO-Siberia 6, no. 1 (July 8, 2020): 11–21. http://dx.doi.org/10.33764/2618-981x-2020-6-1-11-21.
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Simulation computer model of an optoelectronic coordinate measurer for point objects is considered that is a part of the subject recognition system based on biometric features. The presented model allows you to virtually explore and analyze the processes of functioning of the coordinator, which ensures optimal coordination of parameters and characteristics of various parts of the device, as well as to choose the most effective algorithm for processing the resulting digital image by minimizing the error of measuring the coordinates of a single point object. The simulation is based on the Monte Carlo method of multiple statistical tests, which provides most accurate representation of noise processes that occur when receiving and converting optical information in the optical-electronic path of a coordinate device, since these processes, under solid equal conditions, make a decisive contribution to the final measurement error. The principles of the model are described and the results obtained are discussed, as well as the future development of the model and its application for solving problems of optimal system design of biometric recognition systems.
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de-Marcos, Luis, José-Javier Martínez-Herráiz, Javier Junquera-Sánchez, Carlos Cilleruelo, and Carmen Pages-Arévalo. "Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics." Electronics 10, no. 14 (July 7, 2021): 1622. http://dx.doi.org/10.3390/electronics10141622.
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Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results.
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Wu, Chia-Tung, Guo-Hung Li, Chun-Ta Huang, Yu-Chieh Cheng, Chi-Hsien Chen, Jung-Yien Chien, Ping-Hung Kuo, Lu-Cheng Kuo, and Feipei Lai. "Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study." JMIR mHealth and uHealth 9, no. 5 (May 6, 2021): e22591. http://dx.doi.org/10.2196/22591.
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Background The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Objective The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. Methods This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. Results The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. Conclusions Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.
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Jin, Xiaomin, Zhongmin Wang, and Wenqiang Hua. "Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing." Mobile Information Systems 2019 (September 17, 2019): 1–17. http://dx.doi.org/10.1155/2019/8049804.
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Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.
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Dwivedi, Shubhra, Manu Vardhan, and Sarsij Tripathi. "Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques." Open Computer Science 10, no. 1 (August 3, 2020): 220–30. http://dx.doi.org/10.1515/comp-2020-0009.
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AbstractDistributed denial-of-service (DDoS) attacks on the Internet of Things (IoT) pose a serious threat to several web-based networks. The intruder’s ability to deal with the power of various cooperating devices to instigate an attack makes its administration even more multifaceted. This complexity can be further increased while lots of intruders attempt to overload an attack against a device. To counter and defend against modern DDoS attacks, several effective and powerful techniques have been used in the literature, such as data mining and artificial intelligence for the intrusion detection system (IDS), but they have some limitations. To overcome the existing limitations, in this study, we propose an intrusion detection mechanism that is an integration of a filter-based selection technique and a machine learning algorithm, called information gain-based intrusion detection system (IGIDS). In addition, IGIDS selects the most relevant features from the original IDS datasets that can help to distinguish typical low-speed DDoS attacks and, then, the selected features are passed on to the classifiers, i.e. support vector machine (SVM), decision tree (C4.5), naïve Bayes (NB) and multilayer perceptron (MLP) to detect attacks. The publicly available datasets as KDD Cup 99, CAIDA DDOS Attack 2007, CONFICKER worm, and UNINA traffic traces, are used for our experimental study. From the results of the simulation, it is clear that IGIDS with C4.5 acquires high detection and accuracy with a low false-positive rate.
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Bruno, Vito Domenico, Ettorino Di Tommaso, and Raimondo Ascione. "Annuloplasty for mitral valve repair in degenerative disease: to be flexible or to be rigid? That’s still the question." Indian Journal of Thoracic and Cardiovascular Surgery 36, no. 6 (September 18, 2020): 563–65. http://dx.doi.org/10.1007/s12055-020-01001-3.
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Abstract The choice of ring for mitral valve repair is still largely left to the surgeon's preferences and there are no specific guidelines regulating this decision. Despite this previous researches have described important features appertaining to each of the different types of rings currently available. Particularly, the debate is still open in regards to the flexibility that these devices should or should not have. Later in this issue of the Journal, Panicker and colleagues have reported their results with flexible and rigid rings in mitral valve repair. The results are very interesting and once again are highlighting the importance of using the right ring for the right disease.