Data-driven prediction of chemically relevant compositions in multi-component systems using tensor embeddings by Hiroyuki Hayashi, Isao Tanaka

“…By applying tensor decomposition and machine learning techniques, we transformed pseudo-binary oxide compositions from the Inorganic Crystal Structure Database (ICSD) into tensor representations, capturing key chemical trends such as oxidation states and periodic positions. …”
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Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. …”
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Classifying the CP properties of the ggH coupling in H + 2j production by Henning Bahl, Elina Fuchs, Marc Hannig, Marco Menen

“…To improve the constraints on the $\mathcal{CP}$ structure of this coupling, we investigate Higgs production with two jets using machine learning. In particular, we exploit the $\mathcal{CP}$ sensitivity of the so far neglected phase space region that differs from the typical vector boson fusion-like kinematics. …”
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The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning by Ghassan Samara, Essam Al-Daoud, Nael Swerki, Dalia Alzu’bi

“…We compare our proposed model to different deep learning and machine learning methodologies. Our proposed model outperformed the competing models with an accuracy of 99.66%, compared to the support vector machine’s accuracy of 99%.…”
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Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network by Hector Hutin, Pavlo Bilous, Chengzhi Ye, Sepideh Abdollahi, Loris Cros, Tom Dvir, Tirth Shah, Yonatan Cohen, Audrey Bienfait, Florian Marquardt, Benjamin Huard

“…Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. …”
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HTTP malicious traffic detection method based on hybrid structure deep neural network by Jia LI, Xiaochun YUN, Shuhao LI, Yongzheng ZHANG, Jiang XIE, Fang FANG

“…In response to the HTTP malicious traffic detection problem,a preprocessing method based on cutting mechanism and statistical association was proposed to perform statistical information correlation as well as normalization processing of traffic.Then,a hybrid neural network was proposed based on the combination of raw data and empirical feature engineering.It combined convolutional neural network (CNN) and multilayer perceptron (MLP) to process text and statistical information.The effect of the model was significantly improved compared with traditional machine learning algorithms (e.g.,SVM).The F₁value reached 99.38% and had a lower time complexity.At the same time,a data set consisting of more than 450 000 malicious traffic and more than 20 million non-malicious traffic was created.In addition,prototype system based on model was designed with detection precision of 98.1%~99.99% and recall rate of 97.2%~99.5%.The application is excellent in real network environment.…”
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Short-term prediction network for short-wave MUF based on model-data dual-driven by Junbing LI, Youjun ZENG, Xiaoping ZENG, Guojun LI, Chenxi BAI

“…Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidirectional gated recurrent unit (BiGRU) network for short-term prediction of MUF was proposed.On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error (RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model.…”
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Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics by Vitali Chaiko

“…In recent years, machine learning has emerged as a powerful tool for such predictions. …”
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Editorial: A New Chapter for IJIMAI by Rubén González-Crespo

“…Throughout these volumes, IJIMAI has maintained a rigorous approach and an interdisciplinary vision, allowing us to address relevant topics from artificial intelligence and machine learning to interactive multimedia and complex systems. …”
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From pixels to prognosis: radiomics and AI in Alzheimer’s disease management by Danting Peng, Weiju Huang, Ren Liu, Wenlong Zhong

“…Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. …”
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Quantum tug of war between randomness and symmetries on homogeneous spaces by Rahul Arvind, Kishor Bharti, Jun Yong Khoo, Dax Enshan Koh, Jian Feng Kong

“…Finally, as a practical demonstration of our findings, we study the expressibility of quantum machine learning Ansätze in homogeneous spaces. Our work provides a fresh perspective on the relationship between randomness and symmetry in the quantum world.…”
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Analysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence by Cristina Manresa-Yee, Silvia Ramis, José M. Buades

“…Potential uses of automated Facial Expression Recognition (FER) cover a wide range of applications such as customer behavior analysis, healthcare applications or providing personalized services. Data for machine learning play a fundamental role, therefore, understanding the relevancy of the data in the outcomes is of utmost importance. …”
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Research on Modern Book Packaging Design under Aesthetic Evaluation Based on Deep Learning Model by Yuanyuan Li

“…To this end, this paper introduces the machine learning algorithms used in this paper, including the AdaBoost algorithm and the SVR algorithm. …”
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Improved Double-Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism by Geqiao Liu, Mingjie Tan

“…Multilabel classification is a key research topic in the machine learning field. In this study, the author put forward a two/two-layer chain classification algorithm with optimal sequence based on the attention mechanism. …”
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PENERAPAN ARTIFICIAL INTELLIGENCE (AI) DALAM PERAMALAN AKUNTANSI TINJAUAN LITERATUR DAN AGENDA PENELITIAN MASA DEPAN by Gilang Surya Pratama, Agus Munandar

“…The analysis results show significant improvements in forecasting accuracy using AI technology, with Machine Learning achieving 78% accuracy in revenue forecasting, Deep Learning 85% in financial trend prediction, and Natural Language Processing 89% in sentiment analysis. …”
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Artificial Intelligence (AI)-driven approach to climate action and sustainable development by Haein Cho, Emmanuel Ackom

“…An Artificial Intelligence (AI)-based approach is proposed in this study to explore the interconnectedness by applying machine learning classifier and natural language processing. …”
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Defending Deep Neural Networks Against Backdoor Attack by Using De-Trigger Autoencoder by Hyun Kwon

“…Experiments were conducted using MNIST, Fashion-MNIST, and CIFAR-10 as the experimental datasets and TensorFlow as the machine learning library. For MNIST, Fashion-MNIST, and CIFAR-10, respectively, the proposed method detected 91.5%, 82.3%, and 90.9% of the backdoor samples and had 96.1%, 89.6%, and 91.2% accuracy on legitimate samples.…”
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Empowering MNOs with AI in 5G era by Siyu PAN, Yunyong ZHANG, Rongfang ZHANG, Di ZHANG

“…As the next generation of telecommunication standard and technology,5G makes it possible for the network to support more advanced technologies like IoT,VR/AR,etc,due to its unique features,high bandwidth and low latency.Those mushrooming new application scenarios and service demand has been bringing MNOs (mobile network operators) huge opportunities as well as challenges.With the development of AI and machine learning technology,MNOs can not only upgrade their own business but also enter more fields with the advantage of “data tunnel” to provide customized solutions for consumers.How AI can empower the MNOs in 5G era from 5 aspects was represented.First of all,the possible challenges brought by 5G were introduced.What entailed was the necessity and feasibility.Then,3 points where MNOs could focus on to carry out AI technologies were discussed,as namely intelligent network,intelligent marketing and service and industrial internet.Finally,the possible difficulties faced by MNOs when applying AI technologies in the future were forecasted.…”
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Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases by Jiaxin Wu, Rui Jiang

“…We classify the existing methods for characterizing nsSNPs into three categories (sequence based, structure based, and annotation based), and we introduce machine learning models for the prediction of deleterious nsSNPs. …”
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Armenia Towards a "New Economy": Defense Industry by A. B. Harutyunyan

“…In fact, a new arms race could start in the world as a side effect of the so-called “4th Industrial Revolution” – the main innovative trend of which is the widespread use of technologies such as artificial intelligence, machine learning, quantum computing, additive manufacturing, robotics, Internet of things, cloud technologies etc., and the countries involved in military conflicts cannot neglect this circumstance. …”
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