Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…The rapid expansion of devices on the Internet of Things (IoTs) has led to a significant rise in IoT botnet attacks, creating an urgent need for advanced detection and classification methods. This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. …”
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Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso, Alejandro Cama-Pinto

“…This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. …”
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Penetration Testing and Machine Learning-Driven Cybersecurity Framework for IoT and Smart City Wireless Networks by Tamara Zhukabayeva, Zulfiqar Ahmad, Aigul Adamova, Nurdaulet Karabayev, Yerik Mardenov, Dina Satybaldina

“…Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. …”
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vBerlinV2N: Recreating a Cellular Network Measurement Campaign With Simulations by Christian L. Vielhaus, Mauri Seidel, Vincent Latzko, Alexander Grob, Peter Sossalla, Martin Reisslein, Frank H. P. Fitzek

“…System-level simulations play a crucial role in evaluating the behaviors of cellular networks, yet most studies rely on synthetic simulation scenarios rather than reproducing realistic network deployments. …”
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Integration of Artificial Neural Network Regression and Principal Component Analysis for Indoor Visible Light Positioning by Negasa Berhanu Fite, Getachew Mamo Wegari, Heidi Steendam

“…Leveraging machine learning techniques, particularly regression-based artificial neural networks (ANNs), offer a promising alternative. ANNs excel at modeling the intricate relationships within data, making them well-suited for handling the complex dynamics of indoor lighting environments. …”
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Dietary habits and complex food relations in Northwest China: a population-based network analysis by Xinhua Wang, Duolao Wang, Shaonong Dang, Baibing Mi, Hong Yan, Yuhong Zhang, Yijun Kang, Jianghong Dai, Jing Hui, Samuel Chacha, Huang Yan, Zongkai Li, Jiaxin Cai, Fuchang Ma

“…The staple food-related food network indicated that the intake of rice, whole grains and beans, and potatoes was positively correlated with the intake of most other foods, while intake of wheat was negatively correlated with foods of animal source of food, milk and dairy products.Conclusions Northwest China’s diet exhibits irrational patterns, highlighting the importance of assessing overall dietary patterns in nutritional evaluation.…”
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Resilience evolution and optimization strategies of ecological networks in the Three Gorges Reservoir Area: A scenario-based simulation approach by Haohua Wang, Lulu Zhou, Kangchuan Su, Yun Zhou, Qingyuan Yang

“…This study focuses on the Three Gorges Reservoir Area (TGRA), addressing human-land relationship conflicts by constructing an EN system and analyzing its spatiotemporal evolution from 2001 to 2023. To evaluate network resilience, this study employs node attack simulation methods, we dynamically assessed EN resilience through four functional and structural indicators. …”
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Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning by Fudong Ren, Koichi Isobe

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Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn’s disease and intestinal tuberculosis: insights from an integrated multi-coho... by Aditya Bajaj, Manasvini Markandey, Amit Samal, Sourav Goswami, Sudheer K. Vuyyuru, Srikant Mohta, Bhaskar Kante, Peeyush Kumar, Govind Makharia, Saurabh Kedia, Tarini Shankar Ghosh, Vineet Ahuja

“…Module reproducibility was reinvestigated through meta-network analysis encompassing >5400 bacteriomes and ~900 mycobiomes. …”
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An Evaluation and Implementation of Rule-Based Home Energy Management System Using the Rete Algorithm by Tomoya Kawakami, Naotaka Fujita, Tomoki Yoshihisa, Masahiko Tsukamoto

“…The Rete algorithm is a typical pattern matching algorithm for IF-THEN rules. Currently, we have proposed a rule-based Home Energy Management System (HEMS) using the Rete algorithm. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…For ARs, relevant patterns include elongated bands of high TMQ and eastward winds. …”
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Reinforced liquid state machines—new training strategies for spiking neural networks based on reinforcements by Dominik Krenzer, Martin Bogdan, Martin Bogdan, Martin Bogdan

“…IntroductionFeedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.MethodsThe Reinforced Liquid State Machine architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a novel reward-based reinforcement system to enhance learning efficacy. …”
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Integrated deep network model with multi-head twofold attention for drug–target interaction prediction by Angelin Jeba P, Tamilpavai G

“…The MHTA mechanism enhances the model’s ability to focus on different aspects of drug and target features independently, effectively capturing intricate interaction patterns. Dense embeddings generated from input representations are refined using recurrent layers for long-range dependencies and convolutional layers for local patterns. …”
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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

“…In this research, we introduce a real-time fraud detection framework that combines Adaptive Graph Neural Networks (GNNs) and Federated Learning (FL) to overcome these limitations. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…Compared to other machine learning algorithms, the proposed models demonstrate superior performance in balancing accuracy, computational efficiency, and detection speed, making them ideal for real-time network security applications. This approach’s flexibility to adapt to various network conditions and datasets ensures consistent optimal performance as traffic patterns change.…”
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Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks by Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen

“…The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. …”
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ChaMTeC: CHAnnel Mixing and TEmporal Convolution Network for Time-Series Anomaly Detection by Ibrahim Delibasoglu, Deniz Balta, Musa Balta

“…Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. …”
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Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm by Hassan Ghaedi, Seyed Reza Kamel Tabbakh, Reza Ghaemi

“…Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). …”
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Intrusion Alert Analysis Method for Power Information Communication Networks Based on Data Processing Units by Rui Zhang, Mingxuan Zhang, Yan Liu, Zhiyi Li, Weiwei Miao, Sujie Shao

“…Finally, the suffix tree models attack activities, capturing key behavioral paths of high-severity alerts and identifying attacker patterns. Experimental evaluations on the CPTC-2017 and CPTC-2018 datasets validate the proposed method’s effectiveness in reducing alert redundancy, extracting critical attack behaviors, and constructing attack activity sequences. …”
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Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes by Richard Lupat, Rashindrie Perera, Sherene Loi, Jason Li

“…We evaluated our use of Autoencoder against other dimensionality reduction techniques and demonstrated its superiority in learning patterns associated with breast cancer subtypes. …”
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