Modal Passport Concept for Enhanced Non-Destructive Monitoring and Diagnostics of Wind Turbine Blades by Aleksey Mironov, Pavel Doronkin, Aleksejs Safonovs

“…The algorithms and methods of the modal passport discussed in this paper propose a non-destructive technique already used for helicopter blade condition monitoring and diagnostics. …”
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Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial by Kalapala Prasad, V. Ravi Kumar, R. Suresh Kumar, A. S. Rajesh, Anjani Kumar Rai, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, Amjad Iqbal, Dawit Kefyalew

“…Multicomponent adsorption modelling is difficult because it is challenging to anticipate the relationships among the adsorbates in this artificial intelligence-based modelling, a choice among different algorithms. Utilizing various algorithms, many studies assessed the single and binary adsorption of paracetamol on activated carbon. …”
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Privacy-Preserving SGD on Shuffle Model by Lingjie Zhang, Hai Zhang

“…In this paper, we consider an exceptional study of differentially private stochastic gradient descent (SGD) algorithms in the stochastic convex optimization (SCO). …”
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Qualitative Mechanical Problem-Solving by Artificial Agents: by Shreya Banerjee, Selmer Bringsjord, Michael Giancola, Naveen Sundar Govindarajulu

“…The Bennett Mechanical Comprehension Tests (BMCT-I and BMCT-II) assess a human’s ability to solve QMPS problems, and are used in the real world by many employers to evaluate job candidates. Building on the work of others who have attacked BMCT under the rubric of Psychometric AI (PAI), we introduce one of our novel algorithms (A_B1) in a family (A_B) of such for QMPS as required by BMCT, illustrate via case studies, report time-based performance of A_B1, and assess our progress with an eye to future work in which our approach is extended to a sub-class of algorithms in A_B that exploit the power of argument-based nonmonotonic logic, and leverage the success of transformer models to enhance their efficiency.…”
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Rekayasa Perangkat Lunak Aplikasi Presensi Mobile Menggunakan Metode Deep Learning by Ragil Setiawan, Nurcahya Pradana Taufik Prakisya, Rosihan Ariyuana

“…This paper aims to determine the value of the memorization and generalization algorithms model of CNN MobileFaceNet  on the application.  …”
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Resource Scheduling Method for Integration of TT＆C and Observation Based on Multi-Agent Deep Reinforcement Learning by Siyue CHENG, Haoran LI, Weigang BAI, Di ZHOU, Yan ZHU

“…With the development of satellite communication technology and the continuous expansion of the constellation scale, the integration of TT＆amp;C and observation technology has become the mainstream trend.The large constellation scale, many scheduling objects and complex operation joint control bring great challenges to the integrated resource scheduling of satellite network TT＆amp;C and observation.Subject to the low solution effi ciency and complex constraints of scheduling algorithms, the traditional TT＆amp;C resource scheduling technology adopts the advance injection TT＆amp;C instructions to perform tasks according to the fi xed deployment, which is diffi cult to meet the scheduling needs of emergencies and emergency tasks.Therefore, a kind of resource scheduling method based on multi-agent actor-Agent Actor-Critic Deterministic Policy Gradient Algorithms (MADDPG) was presented.With centralized training and distributed execution, the multi-agent model of integrated task of TT＆amp;C and observation was established.By analyzed the scheduling strategy of neighbor agent, the response speed of local information was improved.According to the model and constraints in the integrated resource scheduling problem of TT＆amp;C and observation, selected signifi cant and interpretable constraints, then established the multi-agent resource scheduling reinforcement learning model, and carried on the simulation test.The simulation results showed that the task benefi t of this method was 22% higher than the traditional method.…”
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Introducing a Novel Method to Identify the Future Trend of Nikkei 225 Stock Price in Order to Reduce Investment Risk by Freyr Björgvinsson

“…Stock market forecasting is a challenging task, as many factors influence the course of the stock market, from economic and political to social events. …”
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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

“…With the transition to new generation networks, the issues of improving routing algorithms and protocols seem to be especially relevant. …”
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Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques by Nihat Pamuk

“…The use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, and the new algorithms created by recurrent neural networks and CNNs. …”
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A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment by Arabinda Pradhan, Sukant Kishoro Bisoy, Amardeep Das

“…To solve those problems many researchers have applied different types of scheduling techniques. …”
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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques by Oumoulylte Mariame, El Allaoui Ahmad, Farhaoui Yousef, Boughrous Ali Ait

“…We assess the effectiveness of algorithms such as Random Forest, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gradient Boosting. …”
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Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval by Syed Furqan Rafique, Zhang Jianhua, Rizwan Rafique, Jing Guo, Irfan Jamil

“…In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. …”
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Thermal comfort and energy related occupancy behavior in Dutch residential dwellings by Anastasios Ioannou

“…Such pattern recognition algorithms can be more effective in the era of mobile internet, which allows the capturing of huge amounts of data. …”
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Machine Learning-Based Supervised Classification of Sentinel-2 MSI and Landsat-8 OLI Imagery in Marguerite Bay of Antarctic Peninsula by M. Arkalı, M. E. Atik, Ş. Ö. Atik

“…Especially in the last decade, many innovative advantages of machine learning algorithms have been known, and their use in places where the effects of climate change are closely monitored, such as the polar regions, has introduced revolutionary scientific breakthroughs. …”
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Solving Minimum Cost Three-Dimensional Localization Problem in Ocean Sensor Networks by Chao Zhang, Yingjian Liu, Zhongwen Guo, Yu Wang

“…Current localization algorithms mainly focus on how to localize as many sensors as possible given a set of mobile or static anchor nodes and distance measurements. …”
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Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers by Shan Wang, Sulaiman Khan, Chuyi Xu, Shah Nazir, Abdul Hafeez

“…Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. …”
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landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images by Jef Jonkers, Luc Duchateau, Glenn Van Wallendael, Sofie Van Hoecke

“…Anatomical landmark localization in 2D/3D images is a critical task in medical imaging. Although many general-purpose tools exist for landmark localization in classical computer vision tasks, such as pose estimation, they lack the specialized features and modularity necessary for anatomical landmark localization applications in the medical domain. …”
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Computational Intelligence in Sports: A Systematic Literature Review by Robson P. Bonidia, Luiz A. L. Rodrigues, Anderson P. Avila-Santos, Danilo S. Sanches, Jacques D. Brancher

“…Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.…”
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Modelling Gas Transport in Multiphasic Materials: Application to Semicrystalline Membranes by Lorenzo Merlonghi, Marco Giacinti Baschetti, Maria Grazia De Angelis

“…The description of gas permeation across heterogeneous materials has been studied with many methods, mainly focusing on composites with high aspect ratios and low filler volume fractions. …”
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A case study on entropy-aware block-based linear transforms for lossless image compression by Borut Žalik, David Podgorelec, Ivana Kolingerová, Damjan Strnad, Štefan Kohek

“…Abstract Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. …”
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