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Chavel, Thierry. "La rencontre humaine est-elle soluble dans l’intelligence artificielle ?" Management international 28, no. 2 (2024): 142–44. http://dx.doi.org/10.59876/a-ma53-q5cw.
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Avec la numérisation du monde, la réalité n’est plus ce qu’elle était. Je peux avoir l’illusion d’être ici et ailleurs. Un cloud remplace ma mémoire personnelle. L’autre du débat s’efface au profit du même des communautés virtuelles. La 4e révolution industrielle n’est pas qu’un saut technologique, c’est surtout un choix de société qui renouvelle en profondeur l’exercice du leadership et ses trois fondements humanistes : la fragilité, l’altérité et la responsabilité. L’irruption d’outils de machine learning tels que Chat-GPT transforme violemment les métiers de la prestation intellectuelle. Un algorithme sophistiqué peut désormais produire un langage cohérent, vraisemblable et interactif. A l’image des avocats pressés, des recruteurs en batterie et des collégiens paresseux, la tribu coach voit muter sa liturgie de la présence. Concrètement, quelle place l’intelligence artificielle (IA) va-t-elle prendre dans l’accompagnement humain ?
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Li, Jiahang. "Research on Interactive System of Movie Subtitle Speech Based on Machine Learning Technology." Frontiers in Computing and Intelligent Systems 2, no. 2 (2022): 22–24. http://dx.doi.org/10.54097/fcis.v2i2.3744.
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The composition elements of subtitles, from the early single text, have developed into the present text, graphics, colors, animation, special effects and other combinations. With the development of speech technology and natural language understanding, speech interaction system has become a hot research field. Different from the traditional data interaction between keyboard, mouse and display, using hearing to transmit data makes the interactive system of movie subtitles more anthropomorphic and intelligent. It is the most natural and convenient means for human beings to exchange information with intelligent systems by incorporating machines and equipment with voice information processing capabilities into human voice interactive objects and endowing movie subtitle interactive systems with biological language recognition functions. A machine learning-based movie subtitle voice interactive system is constructed, which can well expand the application of voice interactive system and improve the user experience. In this paper, a movie subtitle voice interactive system based on machine learning technology is proposed, so as to better and effectively realize human-computer voice interaction.
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Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.
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An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.
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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimental data show that there is a positive interaction between teachers and students. Most students use the interactive communication mode of machines, while a small number of students use real-time interactive discussions with teachers. The experimental results show that the excellent rate of ABC classes in the first semester is 80%, 82% and 90%, respectively, through the machine-supervised teaching mode. Therefore, supervised machine learning can greatly help students improve their academic performance. In the future, we should further explore the application of other personalized and extensible machine learning methods in quality education.
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Amershi, Saleema, James Fogarty, Ashish Kapoor, and Desney Tan. "Effective End-User Interaction with Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1529–32. http://dx.doi.org/10.1609/aaai.v25i1.7964.
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End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.
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Guo, Chao-Yu, and Ke-Hao Chang. "A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2338. http://dx.doi.org/10.3390/ijerph19042338.
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Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not provide the p-value of specific feature interaction. Therefore, we propose a novel machine learning algorithm to assess the p-value of a feature interaction, named the extreme gradient boosting machine for feature interaction (XGB-FI). The first step incorporates the concept of statistical methodology by stratifying the original data into four subgroups according to the two interactive features. The second step builds four XGB machines with cross-validation techniques to avoid overfitting. The third step calculates a newly defined feature interaction ratio (FIR) for all possible combinations of predictors. Finally, we calculate the empirical p-value according to the FIR distribution. Computer simulation studies compared the XGB-FI with the multiple regression model with an interaction term. The results showed that the type I error of XGB-FI is valid under the nominal level of 0.05 when there is no interaction effect. The power of XGB-FI is consistently higher than the multiple regression model in all scenarios we examined. In conclusion, the new machine learning algorithm outperforms the conventional statistical model when searching for an interaction.
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Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.
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Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the interaction distance reduces to a global optimization problem whose goal is to search for the free-fermion entanglement spectrum closest to the given entanglement spectrum. In this work, we employ techniques from machine learning in order to perform this same task. In a supervised learning setting, we use labeled data obtained by computing the interaction distance and predict its value via linear regression. Moving to a semi-supervised setting, we train an autoencoder to estimate an alternative measure to the interaction distance, and we show that it behaves in a similar manner.
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Kumar, Dr Tribhuwan, Klinge Orlando Villalba-Condori, Dennis Arias-Chavez, Rajesh K., Kalyan Chakravarthi M, and Dr Suman Rajest S. "An Evaluation on Speech Recognition Technology based on Machine Learning." Webology 19, no. 1 (2022): 646–63. http://dx.doi.org/10.14704/web/v19i1/web19046.
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Speech is the basic way of interaction between the listener to the speaker by voice or expression. Humans can easily understand the speakers' message, but machines can't understand the speaker's word. Nowadays, most of our lives are occupied by machines; but we can't interact with machines. The human brain, like machine learning technology, is essential for speech recognition to interact with machines to humans. The language used for speech recognition must be a global language, so English has been used in this paper. The machine learning methodology is used in a lot of assignments through the feature learning capability. The data modelling capability results attained supplementary than the performance of normal learning methodology. So, in this work, the speech signal recognition is based on a machine-learning algorithm to merge the speech features and attributes. As a result of voice as a bio-metric implication, the speech signal is converted into a significant element of speech improvement. A new speech and emotion recognition technology is introduced. In this paper, discriminated speaking technology are spotlighted on the feature extraction, improvement, segmentation and progression of speech emotion recognition. Initially, the trained RNN layer-based feature extraction is done to get the speech signal's high-level features. From the generated high-level features are used for generating the new speech feature for the capsule network. Finally, the obtained speech features and attribute features are combined into the same RNN with Caps Net framework through the fully connected network. The experimental result shows the improved proposed speech recognition algorithms accuracy with another state-of-the-art method.
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Coe, J. P. "Machine Learning Configuration Interaction." Journal of Chemical Theory and Computation 14, no. 11 (2018): 5739–49. http://dx.doi.org/10.1021/acs.jctc.8b00849.
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Holzinger, Andreas. "Interactive Machine Learning (iML)." Informatik-Spektrum 39, no. 1 (2015): 64–68. http://dx.doi.org/10.1007/s00287-015-0941-6.
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Dawood, Dr Amina Atiya, and Balasem Alawi Hussain. "Machine Learning for Single and Complex 3D Head Gestures: Classification in Human-Computer Interaction." Webology 19, no. 1 (2022): 1431–45. http://dx.doi.org/10.14704/web/v19i1/web19095.
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This paper presents a new Hidden Markov Model based approach for fast and automatic detection and classification of head movements in real time dynamic videos. The model has been developed to utilize human-computer interaction applications by using only the laptop webcam. The proposed model has the ability to predict single head and combined simultaneously in fast responses. Other models paid more attention to classify head nod and shake only, but our model contribute the role of other head movements. The model proposed here doesn’t need any user intervention or previous knowledge of its environment. In addition, there is no limitation on illumination changes and occlusions, as well as no restrictions on head movements ranges. The model achieved significant results and efficient performances when tested on unseen data. As the model accuracies were 94%, 99%, 83%, 87%, 93%, 96% for all head gestures (rest, nod, turn, shake, tilt and tilting) respectively. On the other hand, the model accuracy was 99% and 88% for combined and single cues respectively. The aim of this model is to provide a fast application to infer and predict human emotions and affective states in real time through head gestures.
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Harvey, Neal, and Reid Porter. "User-driven sampling strategies in image exploitation." Information Visualization 15, no. 1 (2014): 64–74. http://dx.doi.org/10.1177/1473871614557659.
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Both visual analytics and interactive machine learning try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human–computer interaction. This article focuses on one aspect of the human–computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data are to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications, but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this article, we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools toward local minima that have lower error than tools trained with all of the data. In preliminary experiments, we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.
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Zholshiyeva, Lazzat, Zhanat Manbetova, Dinara Kaibassova, et al. "Human-machine interactions based on hand gesture recognition using deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 741. http://dx.doi.org/10.11591/ijece.v14i1.pp741-748.
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Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN), which show excellent results in gesture recognition tasks. Next, the development and implementation of a human-machine interaction system based on hand gesture recognition is discussed. System architectures are described, as well as technical and practical aspects of their application. In conclusion, the article summarizes the research results and outlines the prospects for the development of hand gesture recognition technology to improve human-machine interaction. The advantages and limitations of the technology are analyzed, as well as possible areas of its application in the future.
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Lindvall, Martin, Jesper Molin, and Jonas Löwgren. "From machine learning to machine teaching." Interactions 25, no. 6 (2018): 52–57. http://dx.doi.org/10.1145/3282860.
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Sadhasivam, Jayakumar, Senthil J, Ganesh R.M, and Chellapan N. "Liver Disease Prediction Using Machine Learning Classification." Webology 18, no. 02 (2021): 441–52. http://dx.doi.org/10.14704/web/v18si02/web18293.
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People have disorder of liver that require medical care at correct time. It is utmost important to find the disease before it elapse the curable stage. Significantly, much of understanding of organ development has arisen from analyses of patients with liver deficiencies. Data mining is beneficial to find the disease at early stage based on the factors that can be gathered by performing test on the patient. Nowadays, around 65 % of the population in India are eating junk foods which minimize the metabolism rate and effect liver in many ways. In recent years, liver disorders have excessively increased and are still considered to be life threatening because it has caused low survivability. Still the patients having liver diseases are increasing and the symptoms of the diseases are difficult to identify. The doctors often failed to identify the symptoms which can cause severe damages to the patient and it requires utmost attention. So, we are applying Medical Data Mining (MDM) for predicting the liver disease by using the historical data and understanding their patterns. Here we are using prediction model i.e. Support Vector Machine (SVM) to achieve the goal.
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J, Cynthia, G. Sakthi Priya, C. Kevin Samuel, Suguna M, Senthil J, and S. Abraham Jebaraj. "Traffic Flow Forecasting Using Machine Learning Techniques." Webology 18, no. 04 (2021): 1512–26. http://dx.doi.org/10.14704/web/v18si04/web18295.
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Congestion due to traffic, results in wasted fuel, increase in pollution level, increase in travel time and vehicular queuing. Smart city initiatives are aimed to improve the quality of urban life. Intelligent Transportation System (ITS) provides solution for many smart city projects, as they capture real time data without any fixed infrastructure. The real-time prediction of traffic flow aids in alleviating congestion. Accurate and timely prediction on the future traffic flow helps individual travellers, public transport, and transport planning. Existing systems are designed to predict specific traffic parameters like weekday, weekend, and holidays. This research presents a machine learning based traffic flow forecasting for the city of Bloomington, US not with any precise parameter. The day-wise dataset for the 5 areas is taken from Jan 1, 2017 to Dec 31, 2019. The algorithm used for implementation is Support Vector Regression (SVR) and Long Short-Term Memory (LSTM). LSTM algorithm provides better traffic prediction with least root means square error value.
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Baker del Aguila, Ryan, Carlos Daniel Contreras Pérez, Alejandra Guadalupe Silva-Trujillo, Juan C. Cuevas-Tello, and Jose Nunez-Varela. "Static Malware Analysis Using Low-Parameter Machine Learning Models." Computers 13, no. 3 (2024): 59. http://dx.doi.org/10.3390/computers13030059.
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Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper is to demonstrate the efficacy of memory-optimized machine learning solutions for the task of static analysis of software metadata. The study comprises an evaluation and comparison of the performance metrics of three popular machine learning solutions: artificial neural networks (ANN), support vector machines (SVMs), and gradient boosting machines (GBMs). The study provides insights into the effectiveness of memory-optimized machine learning solutions when detecting previously unseen malware. We found that ANNs shows the best performance with 93.44% accuracy classifying programs as either malware or legitimate even with extreme memory constraints.
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Thieme, Anja, Danielle Belgrave, Akane Sano, and Gavin Doherty. "Machine learning applications." Interactions 27, no. 2 (2020): 6–7. http://dx.doi.org/10.1145/3381342.
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Abdullah, Syahid, Wisnu Ananta Kusuma, and Sony Hartono Wijaya. "Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning." Jurnal Teknologi dan Sistem Komputer 10, no. 1 (2022): 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.
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Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.
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Gillies, Marco. "Understanding the Role of Interactive Machine Learning in Movement Interaction Design." ACM Transactions on Computer-Human Interaction 26, no. 1 (2019): 1–34. http://dx.doi.org/10.1145/3287307.
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Bai, Xiuyan, and Jiawen Shi. "A Machine Learning Based Method to Evaluate Learning in Gamification Practices." International Journal of Emerging Technologies in Learning (iJET) 18, no. 21 (2023): 171–85. http://dx.doi.org/10.3991/ijet.v18i21.44689.
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With the integration of advanced methods and technologies in higher vocational education, educational gamification has emerged as a new approach to encourage students’ active participation in learning. However, it is difficult to accurately evaluate student participation in this environment and delve into the process of interactive evolution. Most existing research methods primarily focus on qualitative analysis, while attempts to conduct quantitative analysis are often constrained by traditional statistical methods. Moreover, these methods frequently fail to consider the interactive dynamics that occur between teachers and students. This study proposes a method to evaluate learning participation in educational gamification. K-means clustering was used, and a framework for educational gamification was constructed using a process interaction evolutionary game. By conducting a thorough analysis of the interactive dynamics between teachers and students, this study offers practical guidance and strategies for educators.
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Phongying, Methaporn, and Sasiprapa Hiriote. "Diabetes Classification Using Machine Learning Techniques." Computation 11, no. 5 (2023): 96. http://dx.doi.org/10.3390/computation11050096.
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Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients’ data can be analyzed to find patterns or facts that are difficult to explain, making diagnoses more reliable and convenient. The purpose of this research was to compare the efficiency of diabetic classification models using four machine learning techniques: decision trees, random forests, support vector machines, and K-nearest neighbors. In addition, new diabetic classification models are proposed that incorporate hyperparameter tuning and the addition of some interaction terms into the models. These models were evaluated based on accuracy, precision, recall, and the F1-score. The results of this study show that the proposed models with interaction terms have better classification performance than those without interaction terms for all four machine learning techniques. Among the proposed models with interaction terms, random forest classifiers had the best performance, with 97.5% accuracy, 97.4% precision, 96.6% recall, and a 97% F1-score. The findings from this study can be further developed into a program that can effectively screen potential diabetes patients.
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Wicaksono, Mochamad Fajar, Myrna Dwi Rahmatya, and Angga Rinaldi. "Interactive Letter and Number Learning Machines For Early Childhood." CCIT Journal 13, no. 2 (2020): 220–32. http://dx.doi.org/10.33050/ccit.v13i2.1057.
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The purpose of this study is to design and create interactive learning machines for letters and numbers for early childhood. The machine designed is an interactive learning machine so that early childhood is interested in learning and can learn independently. Arduino Mega2560 is used as the main processor on this machine. On this machine, there are three modes, namely learning mode, question mode, and counting mode. In learning mode, Arduino will read every button input pressed, display on the LCD and make a sound through DF Player in accordance with the input received. In the question mode, Arduino will issue a question in the form of sound and wait for an answer via the input button found on the device. In arithmetic mode, Arduino will read the input of 10 LDR sensors covered by ice-cream sticks placed by young children and make sounds related to the amount. Questions on tools can be changed by using an android application designed in this study. Based on the test results, this tool has been running well where all modes can be run 100% whether it is learning mode, question mode, and counting mode.
 Keyword— Arduino, Early childhood, LCD, DF Player, Letters, and Numbers, Counting
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Teso, Stefano, and Oliver Hinz. "Challenges in Interactive Machine Learning." KI - Künstliche Intelligenz 34, no. 2 (2020): 127–30. http://dx.doi.org/10.1007/s13218-020-00662-x.
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Ibrahim, Dr Abdul-Wahab Sami, and Dr Baidaa Abdul khaliq Atya. "Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques." Webology 19, no. 1 (2022): 1493–503. http://dx.doi.org/10.14704/web/v19i1/web19100.
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Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image processing technique is used to extract the feature from the leaf images. Further for the classification of diseases various machine learning algorithm like the random forest, J48 and support vector machine is used and the result is compared among different machine learning algorithm. After model evaluation, classification accuracy is verified using the n-fold cross-validation technique.
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Duan, Jiashun, and Xin Zhang. "A Class-Incremental Learning Method for Interactive Event Detection via Interaction, Contrast and Distillation." Applied Sciences 14, no. 19 (2024): 8788. http://dx.doi.org/10.3390/app14198788.
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Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods.
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V., Dr Suma. "COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 131–39. http://dx.doi.org/10.36548/jtcsst.2019.2.006.
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The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.
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Stephens, Keri, Anastazja Harris, Amanda Hughes, et al. "Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication." Human-Machine Communication 6 (July 1, 2023): 65–85. http://dx.doi.org/10.30658/hmc.6.5.
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Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication.
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Fung, Lee Hua, and Seetha Letchumy M. Belaidan. "Sentiment Analysis in Online Products Reviews Using Machine Learning." Webology 18, SI05 (2021): 914–28. http://dx.doi.org/10.14704/web/v18si05/web18271.
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Online Shopping is a phenomenon that is growing rapidly. It refers to the act of buying and selling products or services over the internet. Since customers are shopping online, there are some problems with this process. Firstly, is that customers can fall into fraud and security concerns as there is an inability to inspect the goods that you are purchasing beforehand. There is also the other issue on the quality of the product, this is because when selling online, only simple pictures and or descriptions of the product are all a customer can rely on when purchasing. There is also another factor customer can look at before purchasing a product and those are reviews left by previous customers that have purchased the same product from the same seller. Reviews are left by a consumer that has experienced or purchased a product from the store. Thus, by reading the reviews of a product, the new customer can see whether people liked the product or not, or to see if the product that was delivered was the promised product by the store. With the help of Machine Learning Techniques, the researcher can then try to find the best technique that can be used for Sentiment Analysis on Online Product Reviews.
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Yarimizu, Masayuki, Cao Wei, Yusuke Komiyama, et al. "Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine." Advances in Bioinformatics 2015 (August 11, 2015): 1–5. http://dx.doi.org/10.1155/2015/528097.
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Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP) and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs), and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.
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Yang, Humin, Achyut Shankar, and Velliangiri S. "Artificial Intelligence-Enabled Interactive System Modeling for Teaching and Learning Based on Cognitive Web Services." International Journal of e-Collaboration 19, no. 2 (2023): 1–18. http://dx.doi.org/10.4018/ijec.316655.
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The future of modern education and web-based learning is inherently associated with the advancement in modern technologies and computing capacities of new smart machines, such as artificial intelligence (AI). AI is a high-performance computing environment powered by special processors that use cognitive computing for machine learning and data analytics. There are major challenges in online or web-based learning, such as flexibility, student support, classification of teaching, and learning activities. Hence, this paper proposes smart web-based interactive system modeling (SWISM)based on artificial intelligence for teaching and learning. The paper aimed to categorize learners according to their learning skills and discover how to enable learners with machine learning techniques to have appropriate, quality learning objects. Furthermore, local weight, linear regression, and linear regression methods have been introduced to predict the student learning performance in a cloud platform.
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Nivas, K., M. Rajesh Kumar, G. Suresh, T. Ramaswamy, and Yerraboina Sreenivasulu. "Facial Emotion Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 427–33. http://dx.doi.org/10.22214/ijraset.2023.48585.
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Abstract: The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure convolutional neural network approach outperformed the results of other statistical methods obtained by other authors, including feature engineering. The use of convolutional networks includes a learning function. This looks very promising for this task where the functionality is not easy to define. Additionally, the network he was evaluated using two different corpora. One was used during network training and also helped tune parameters and define the network architecture. This corpus consisted of mimetic emotions. The network that yielded the highest classification accuracy results was tested on the second dataset. Although the network was trained on only one corpus, the network reported promising results when tested on another dataset showing non-real facial emotions. The results achieved did not correspond to the state of the art. Collected evidence indicates that deep learning may be suitable for facial expression classification. Deep learning therefore has the potential to improve human-machine interaction. Because the ability to learn functions allows machines to evolve cognition. And through perception, the machine could offer a smoother response, greatly improving the user's experience.
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Sundar, Balapuri Shiva. "Emotion Detection on text using Machine Learning and Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2277–86. http://dx.doi.org/10.22214/ijraset.2022.44293.
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Abstract: Emotion detection on text is an important field of research in Artificial Intelligence and human-computer interaction. Emotions play key role in human interaction. Emotion detection is closely associated with sentiment detection, in which we detect the polarity of the text. But in emotion detection, we detect emotions such as joy, love, surprise, sadness, fear, and anger. Emotion detection helps the machines to understand human behavior and ultimately it provides users with emotional awareness feedback. In this paper, we are going to compare Machine Learning and Deep Learning techniques with their accuracy and f1 scores. The experimental results show the results provided by deep learning techniques (Bi LSTM and Bi GRU) with word embeddings are more accurate than the other techniques.
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Porter, Reid, James Theiler, and Don Hush. "Interactive Machine Learning in Data Exploitation." Computing in Science & Engineering 15, no. 5 (2013): 12–20. http://dx.doi.org/10.1109/mcse.2013.74.
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de Wit, Paulus A. J. M., and Roberto Moraes Cruz. "Learning from AF447: Human-machine interaction." Safety Science 112 (February 2019): 48–56. http://dx.doi.org/10.1016/j.ssci.2018.10.009.
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Glikis, Rafael, Christos Makris, and Nikos Tsirakis. "DrCaptcha: An interactive machine learning application." Computer Science and Information Systems, no. 00 (2020): 48. http://dx.doi.org/10.2298/csis200130048g.
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The creation of a Machine Learning system is a typical process that is mostly automated. However, we may address some problems in the during development, such as the over-training on the training set. A technique for eliminating this phenomenon is the assembling of ensembles of models that cooperate to make predictions. Another problem that almost always occurs is the necessity of the human factor in the data preparation process. In this paper, we present DrCaptcha [15], an interactive machine learning system that provides third-party applications with a CAPTCHA service and, at the same time, uses the user's input to train artificial neural networks that can be combined to create a powerful OCR system. A different way to tackle this problem is to use transfer learning, as we did in one of our experiments [33], to retrain models trained on massive datasets and retrain them in a smaller dataset.
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Adawy, Mohammad, Hasan Abualese, Nidhal Kamel Taha El-Omari, and Abdulwadood Alawadhi. "Human-Robot Interaction (HRI) using Machine Learning (ML): a Survey and Taxonomy." International Journal of Advances in Soft Computing and its Applications 16, no. 3 (2024): 166–82. http://dx.doi.org/10.15849/ijasca.241130.11.
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Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the humanrobot interaction in a flexible manner. For the sake of reflecting and shaping the interactions between humans and robots, HRI is based on the fusion of the two areas: the people's behaviour and attitudes towards using these robots, as well as the physical, technological, and interactive features of the robots. As the robot has tightly integrated from a set of sensors that collect the data from the environment and send them to the processor which in turn translates the collected data into information that can be used in the robot itself, machine learning (ML) is a well-known research area that focuses on the building of well-stocked knowledge systems by using supervised and unsupervised algorithms. From a conceptual standpoint, this research survey and taxonomy pursue to present an in-depth evaluation and review of the most current state-of-the-art papers that have already been published so far and encompass the use of ML algorithms in the HRI field. Thus, a total of 30 research papers devoted to HRI were examined and analysed to give the most ML algorithms implemented in the field of HRI. Evidently, this study shows that the Neural and Reinforcement learning machine algorithms that are used mostly in the recent studies that have an interest in HRI use a machine learning algorithm with a supervised technique in a physical application. There are many challenges facing HRI using ML algorithms, which reduce the use of other ML algorithms such as deep and SVM learning algorithm. Unfortunately, these challenges limit use in social and mobile applications.
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Lürig, Christoph. "Learning Machine Learning with a Game." European Conference on Games Based Learning 16, no. 1 (2022): 316–23. http://dx.doi.org/10.34190/ecgbl.16.1.481.
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AIs playing strategic games have always fascinated humans. Specifically, the reinforcement learning technique Alpha Zero (D.Silver, 2016) has gained much attention for its capability to play Go, which was hard to crack problem for AI for a long time. Additionally, we see the rise of explainable AI (xAI), which tries to address the problem that many modern AI decision techniques are black-box approaches and incomprehensible to humans. Combining a board game AI for the relatively simple game Connect-Four with explanation techniques offers the possibility of learning something about an AI's inner workings and the game itself. This paper explains how to combine an Alpha-Zero-based AI with known explanation techniques used in supervised learning. Additionally, we combine this with known visualization approaches for trees. Alpha-Zero combines a neuronal network and a Monte-Carlo-Search-Tree. The approach we present in this paper focuses on two explanations. The first explanation is a dynamic analysis of the evolving situation, primarily based on the tree aspect, and works with a radial tree representation (Yee et al., 2001). The second explanation is a static analysis that tries to identify the relevant situation elements using the Lime (Local Interpretable Model Agnostic Explanations) approach (Christoforos Anagnostopoulos, 2020). This technique focuses primarily on the neuronal network aspect. The straightforward application of Lime towards the Monte-Carlo-Search-Tree approach would be too compute-intensive for interactive applications. We suggest a modification to accommodate search trees and sacrifice the model agnosticism specifically. We use a weighted Lasso-based approach on the different board constellations analyzed in the search tree by the neuronal network to get a final static explanation of the situation. Finally, we visually interpret the resulting linear weights from the Lasso analysis on the game board. The implementation is done in Python using the PyGame library for visualization and interaction implementation. We implemented the neuronal networks with PyTorch and the Lasso analysis with Scikit Learn. This paper provides implementation details on an experimental approach to learning something about a game and how machines learn to play a game.
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Raj, Rishav, Vivek Herenj, Vikash Roy, and Manoj Mishra. "Personality Prediction System Using Machine Learning." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 819–24. https://doi.org/10.26562/ijirae.2024.v1111.05.
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A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice-based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric data further enhance the accuracy of these systems, with applications spanning areas like human-computer interaction, marketing, recruitment, and mental health. With the aid of artificial intelligence (AI) and machine learning, these systems are increasingly capable of delivering personalized insights, offering the potential for a wide range of applications from customer engagement to psychological well-being. This abstract outline the core mechanisms, applications, and future directions of personality prediction systems, highlighting their potential to transform how individuals are understood and interacted with in both digital and real-world contexts.
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M, Senthil Raja, Arun Raj L, and Arun A. "Detection of Depression among Social Media Users with Machine Learning." Webology 19, no. 1 (2022): 250–57. http://dx.doi.org/10.14704/web/v19i1/web19019.
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Mental illnesses are a significant and growing public health concern. They have the potential to tremendously affect a person’s life. Depression, in particular, is one of the major reasons for suicide. In recent times, the popularity of social media websites has burgeoned as they are platforms that facilitate discussion and free-flowing conversation about a plethora of topics. Information and dialogue about subjects like mental health, which are still considered as a taboo in various cultures, are becoming more and more accessible. The objective of this paper is to review and comprehensively compare various previously employed Natural Language Processing techniques for the purpose of classification of social media text posts as those written by depressed individuals. Furthermore, pros, cons, and evaluation metrics of these techniques, along with the challenges faced and future directions in this area of research are also summarized.
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Gupta, Shailja, Manpreet Kaur, Sachin Lakra, and Yogesh Dixit. "A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms." Webology 17, no. 1 (2020): 377–97. http://dx.doi.org/10.14704/web/v17i1/web17011.
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Zhu, Chaoyang. "Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 01–13. http://dx.doi.org/10.17762/ijritcc.v11i7.7736.
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Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction.
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Vatsal, Prince. "Real-Time Human Pose Estimation Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34377.
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Human pose estimation is a pivotal domain within computer vision, underpinning applications from motion capture in cinematic production to sophisticated user interfaces in desktop devices. This research delineates the implementation of real-time human pose estimation within web browsers utilizing TensorFlow.js and the PoseNet model. PoseNet, an advanced machine learning model optimized for browser-based execution, facilitates precise pose detection sans specialized hardware. The primary aim of this study is to integrate PoseNet with TensorFlow.js, achieving efficient real-time pose estimation directly in the browser by leveraging JavaScript, thereby ensuring seamless user interaction and broad accessibility. A modular system architecture is designed, focusing on optimization strategies such as model quantization, asynchronous processing, and on-device computation to enhance performance and privacy preservation. In conclusion, this research establishes a robust framework for deploying PoseNet in web environments, underscoring its potential to revolutionize human-computer interaction within browser-based applications. Our findings contribute significantly to the field of computer vision and machine learning, offering insights into the practical deployment of pose estimation models on widely accessible platforms. Keywords — ReaReal-time Pose Estimation, TensorFlow.js, PoseNet, Machine Learning, Computer Vision, Browser-based Pose Detection, Human-Computer Interaction, Multi-person Tracking, On-device Computation, Asynchronous Processing, Cross-browser Compatibility, Performance Optimization, Privacy-preserving AI, Web-based Machine Learning, Motion Capture, Fitness Tracking, Interactive , Virtual Reality Interfaces, Deep LearningLearning
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Rosenfeld, Avi, Zevi Bareket, Claudia V. Goldman, Sarit Kraus, David J. LeBlanc, and Omer Tsimhoni. "Towards Adapting Cars to their Drivers." AI Magazine 33, no. 4 (2012): 46. http://dx.doi.org/10.1609/aimag.v33i4.2433.
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Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers’ behavior, we found that improved learning models could be developed by adding information on drivers’ demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.
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Ganesan, Srividhya, Raju Dr., and Dr Senthil J. "Prediction of Autism Spectrum Disorder by Facial Recognition Using Machine Learning." Webology 18, no. 02 (2021): 406–17. http://dx.doi.org/10.14704/web/v18si02/web18291.
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Autism is normally characterized as pervading disorder. The role Pervasive implies that the disorder is acute. Autism spectrum disorder (ASD) individuals face difficulties in interacting with others. They also have a problem in responding to the actions, hyperactive and behavioural issues. There have been numerous technological enhancements in prediction of autism traits. This paper focusses on various machine learning methods to classify an autistic child. It mainly focusses on classification models applying VGG16 algorithm of SVM classifier, CNN and Haar Cascade using OpenCV. Using these models, better accuracy was achieved compared to other models of classification.
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Samala, Agariadne Dwinggo, and Mita Amanda. "Immersive Learning Experience Design (ILXD): Augmented Reality Mobile Application for Placing and Interacting with 3D Learning Objects in Engineering Education." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 05 (2023): 22–35. http://dx.doi.org/10.3991/ijim.v17i05.37067.
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The Computer Numerical Control (CNC) machine operates based on a numerical control program generated semi-automatically by a Computer Aided Manufacturing (CAM) system or manually by the operator. Repeated practice is required to operate a CNC machine. For an inexperienced operator (student), the practicum requires extensive use of materials and cutting instruments. In the meantime, the number of laboratory equipment for the CNC programming practicum remains limited, and the 3-axis CNC milling machine training unit is inadequate for the number of students. For this reason, we need a medium that can simulate the machining process to aid students in their early stages of learning. This research aims to create a mobile application for milling machine visualization based on augmented reality. This augmented reality (AR) application, "MM: CTU 3-Axis," or Machine Milling CNC Training Unit 3-Axis, was designed with 3D virtual objects to provide students with knowledge and practical experience. The application development model used Multimedia Development Life Cycle (MDLC). This research yields an Android-based augmented reality (AR) application called "MM: CTU 3-Axis" that does not require markers. The development and verification results demonstrated that this application could aid in learning CNC programming and machining practicum, as well as reduce the cost of using cutting tools and the cost of using materials due to repeated experiments on CNC machines.
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Uvarova, O. V., and S. I. Uvarov. "Machine-learning based interatomic potential for studying of crystal structures properties." Izvestiya Vysshikh Uchebnykh Zavedenii. Materialy Elektronnoi Tekhniki = Materials of Electronics Engineering 23, no. 4 (2021): 304–10. http://dx.doi.org/10.17073/1609-3577-2020-4-304-310.
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In the process of modeling multilayer semiconductor nanostructures, it is important to quickly obtain accurate values the characteristics of the structure under consideration. One of these characteristics is the value of the interaction energy of atoms within the structure. The energy value is also important for obtaining other quantities, such as bulk modulus of the structure, shear modulus etc. The paper considers a machine learning based method for obtaining the interaction energy of two atoms. A model built on the basis of the Gaussian Approximation Potential (GAP) is trained on a previously prepared sample and allows predicting the energy values of atom pairs for test data. The values of the coordinates of the interacting atoms, the distance between the atoms, the value of the lattice constant of the structure, an indication of the type of interacting atoms, and also the value describing the environment of the atoms were used as features. The coordinates of the atoms, the distance between the atoms, the lattice constant of the structure, an indication of the type of interacting atoms, the value describing the environment of the atoms were used as features. The computational experiment was carried out with structures of Si, Ge and C. There were estimated the rate of obtaining the energy of interacting atoms and the accuracy of the obtained value. The characteristics of speed and accuracy were compared with the characteristics that were achieved using the many-particle interatomic potential — the Tersoff potential.
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Koma, Hiroaki, Taku Harada, Akira Yoshizawa, and Hirotoshi Iwasaki. "Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 1 (2019): 13–24. http://dx.doi.org/10.4018/ijcini.2019010102.
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The effectiveness of considering the ambient state of a driving car for evaluating the driver's cognitive distracted state is evaluated. In this article, Support Vector Machines and Random Forest, which are representative machine learning models, are applied. As input data for the machine learning model, in addition to a driver's biometric data and car driving data, an ambient state data of a driving car are used. The ambient state data of a driving car considered in this study are that of the preceding car and the shape of the road. Experiments using a driving simulator are conducted to evaluate the effectiveness of considering the ambient state of a driving car.
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Sundar, S. Shyam. "Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)." Journal of Computer-Mediated Communication 25, no. 1 (2020): 74–88. http://dx.doi.org/10.1093/jcmc/zmz026.
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Abstract Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.
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Yang, Yimin. "Heart Disease Prediction and GUI Interaction based on Machine Learning." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 209–18. http://dx.doi.org/10.62051/z195z943.
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Machine learning is now being used to detect heart disease. Considering that failure to diagnose a heart disease patient can lead to serious consequences, including delayed treatment, worsening of the condition, and even life-threatening situations, it is crucial to ensure that as many true patients as possible are confirmed. Thus, it is important to consider recall rates while maintaining a focus on accuracy. This article compares the application of four machine learning models, including Decision Trees (DT), Random Forests (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), in heart disease prediction, and measures the effectiveness of these models by using accuracy, recall rates, and F1 score. The outcomes of the experiment reveal that the SVM model performs the best with a recall rate of 0.97. The balance of the model ensures that it achieves high recall without affecting accuracy. In addition, the author combines it with a Graphical User Interface (GUI) to achieve interactive effects. The model and its interactive functions selected in this experiment can easily avoid missing patients in the first screening and improve the accuracy of disease diagnosis.