Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms by Manny Villa, Eduardo Casilari

“…This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. …”
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Nipple adenoma detected by multimodal ultrasound: a case report and literature review by Jianghao Lu, Jingwen Zhang, Tingting Wu, Yuqin Ma, Peng Zhou

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Multi-step attack detection method based on network communication anomaly recognition by Ankang JU, Yuanbo GUO, Tao LI, Ziwei YE

“…In view of the characteristics of internal fixed business logic,inbound and outbound network access behavior,two classes and four kinds of abnormal behaviors were defined firstly,and then a multi-step attack detection method was proposed based on network communication anomaly recognition.For abnormal sub-graphs and abnormal communication edges detection,graph-based anomaly analysis and wavelet analysis method were respectively proposed to identify abnormal behaviors in network communication,and detect multi-step attacks through anomaly correlation analysis.Experiments are carried out on the DARPA 2000 data set and LANL data set to verify the results.The experimental results show that the proposed method can effectively detect and reconstruct multi-step attack scenarios.The proposed method can effectively monitor multi-step attacks including unknown feature types.It provides a feasible idea for detecting complex multi-step attack patterns such as APT.And the network communication graph greatly reduces the data size,it is suitable for large-scale enterprise network environments.…”
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Deep-learning-based detection of underwater fluids in multiple multibeam echosounder data by Tyméa Perret, Gilles Le Chenadec, Arnaud Gaillot, Yoann Ladroit, Stéphanie Dupré

“…The results demonstrate first that this method surpasses current machine learning techniques, such as Haar-Local Binary Pattern Cascade. Additionally, we thoroughly analyzed the composition of the training dataset and evaluated the detection performance based on various training configurations. …”
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Comprehensive Evidence That Detecting Urban Signals in Large‐Scale Warming Is Highly Uncertain by Pengke Shen, Shuqing Zhao, Zhiyu Xu, Xueqin Zhang, Bo Lu, Zhenyu Han, Menglin Si, Jiashuang Yuan, Lianchun Song, Shuguang Liu

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Multiplex real-time single nucleotide polymorphism detection and quantification by quencher extension by Knut Rudi, Monika Zimonja, Sigrun E. Hannevik, Signe M. Drømtorp

“…We evaluated the SNP-allele pattern in 252 different L. monocytogenes strains. …”
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Advances in fault detection techniques for automated manufacturing systems in industry 4.0 by Yassmin Seid Ahmed, Abba A. Abubakar, Abul Fazal M. Arif, Fadi A. Al-Badour

“…Several methods can be used to detect trends or patterns in any given process and determine if the process variable is within normal limits. …”
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Detecting eavesdropping nodes in the power Internet of Things based on Kolmogorov-Arnold networks. by Rong Wang, Weibin Jiang, Yanjin Shen, Qiqing Yue, Kan-Lin Hsiung

“…Traditional eavesdropping detection methods struggle to adapt to complex and dynamic attack patterns, necessitating the exploration of more intelligent and efficient anomaly localization approaches. …”
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CLIMATE CHANGE DETECTION IN FOUR LOCATIONS IN PENINSULAR MALAYSIA FOR GREEN SUSTAINABILITY AWARENESS by Noor Kesuma Mohd Yazid, Norshahida Shaadan, Firdaus Mohamad Hamzah, Nurain Ibrahim, Mahayaudin M.Mansor

“…The methodology involved investigating the pattern of climate variation using several visualization tools, the Mann-Kendall test, the EWMA control chart, and the Kruskall-Wallis test. …”

Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions by Ruslan Shevchuk, Vasyl Martsenyuk, Bogdan Adamyk, Vladlena Benson, Andriy Melnyk

“…The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. …”
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Detection of the TYR Gene as a Candidate Causal Mutation for Albinism in Taro Cattle by Muhammad Aulia Reza, Ronny Rachman Noor, Jakaria Jakaria, Endang Tri Margawati

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Parts-per-quadrillion level gas molecule detection: CO-LITES sensing by Haiyue Sun, Shunda Qiao, Ying He, Xiaorong Sun, Yufei Ma

“…The artificial fish swarm algorithm auto-designed multi-pass cell (MPC) with double helix pattern, and the polymer modified round-head quartz tuning fork (QTF) with low-resonant frequency (f 0) were adopted to improve the gas absorption and QTF’s detection ability. …”
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A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas by Yun Xiao, Rongqiao Li, Jinyan Li

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Deep Embedded Auto-encoder for End-to-End Unsupervised Image Anomaly Detection by Xuan Huang, Hailin Tang

“…Abstract Image anomaly detection plays a critical role in industrial quality control, medical diagnostics, and security surveillance, yet existing unsupervised methods often suffer from limited detection accuracy and poor adaptability. …”
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A Color-Based Multispectral Imaging Approach for a Human Detection Camera by Shuji Ono

“…Because our color-driven detection relies on pixel-wise spectral reflectance rather than spatial patterns, it remains computationally efficient. …”
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A hybrid Bi-LSTM and RBM approach for advanced underwater object detection. by Manimurugan S, Karthikeyan P, Narmatha C, Majed M Aborokbah, Anand Paul, Subramaniam Ganesan, Rajendran T, Mohammad Ammad-Uddin

“…The proposed approach introduces a hybrid model for Underwater Object Detection (UOD), combining Bi-directional Long Short-Term Memory (Bi-LSTM) with a Restricted Boltzmann Machine (RBM). …”
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ML-based Plant Stress Detection from IoT-sensed Reduced Electromes by Marcos De Oliveira Jr, Gregory Sedrez, Gerson Geraldo H. Cavalheiro

“…The recognition of patterns in the electrical activities of plants (electromes, in time series format) has gained prominence in recent years. …”
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Adaptive Convolution Kernels Construction Based on Unsupervised Learning for Underwater Acoustic Detection by Hao Yin, Chao Li, Haibin Wang, Jun Wang, Fan Yin, Zili Qin, Chuxian Wang

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Experience in deciphering the Betula pubescens resistograms for the detection of trunk rot in woody plants by Grevtsova Vera, Yakovenko Nataliya, Natsentova Elizaveta, Starukhina Ekaterina

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Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey by Ahmed Sharafeldeen, Justin Keowen, Ahmed Shaffie

“…Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential for detecting AD using patient’s speech signals, as subtle changes in speech patterns, such as reduced fluency, pronunciation difficulties, and cognitive decline, can serve as early indicators of the disease, offering a non-invasive and cost-effective method for early diagnosis. …”
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