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Paradoxical roles of inhibitory autapse and excitatory synapse in formation of counterintuitive anticipated synchronization

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Chinese Physics B 2023年第8期32卷 499-509页

作者：丁学利古华光李玉叶贾雁兵 Department of Public Basic Education Fuyang Institute of TechnologyFuyang 236031China School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China College of Mathematics and Computer Science Chifeng UniversityChifeng 024000China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Different from the common delayed synchronization(DS)in which response appears after stimulation,anticipated synchronization(AS)in unidirectionally coupled neurons denotes a counterintuitive phenomenon in which response of the receiver neuron appears before stimulation of the sender neuron,showing an interesting function of brain to anticipate the *** dynamical mechanism for the AS remains unclear due to complex dynamics of inhibitory and excitatory *** this article,the paradoxical roles of excitatory synapse and inhibitory autapse in the formation of AS are ***,in addition to the common roles such that inhibitory modulation delays and excitatory modulation advances spike,paradoxical roles of excitatory stimulation to delay spike via type-II phase response and of inhibitory autapse to advance spike are obtained in suitable parameter regions,extending the dynamics and functions of the excitatory and inhibitory ***,AS is related to the paradoxical roles of the excitatory and inhibitory modulations,presenting deep understandings to the *** autapse induces spike of the receiver neuron advanced to appear before that of the sender neuron at first,and then excitatory synapse plays a delay role to prevent the spike further advanced,resulting in the AS as the advance and delay effects realize a dynamic ***,inhibitory autapse with strong advance,middle advance,and weak advance and delay effects induce phase drift(spike of the receiver neuron advances continuously),AS,and DS,respectively,presenting comprehensive relationships between AS and other *** results present potential measures to modulate AS related to brain function.

关键词： anticipated synchronization inhibitory autapse excitatory synapse paradoxical firing

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Enhancing Machine Learning and Deep Learning Models For Depression Detection: A Focus on SMOTE, RoBERTa, and CNN-LSTM

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 1-18页

作者： Taoussi, Chaimae Lyaqini, Soufiane Metrane, Abdelmoutalib Hafidi, Imad Laboratory of Process Engineering Computer Science and Mathematics University Sultan Moulay Slimane Beni Mellal Morocco Faculty of Sciences and Technology Cadi Ayyad University Marrakech Morocco

Depression is a major public health concern, affecting millions worldwide, and necessitates early, accurate detection for timely intervention. This study focuses on enhancing machine learning (ML) and deep learning (DL) models for improved accuracy in depression detection using the Counsel Chat Dataset. To address the challenges of class imbalance, we employed advanced preprocessing techniques, including the Synthetic Minority Oversampling Technique (SMOTE), alongside model fine-tuning and architectural optimizations. Our results demonstrated significant performance improvements, particularly with transformerbased models and hybrid architectures. RoBERTa, a transformer-based model, achieved an accuracy of 91.55%, an F1-score of 0.91, and a recall of 92.10%, outperforming state-of-the-art approaches. Similarly, CNN-LSTM attained an accuracy of 91.67% with a 95% CI of (0.8987, 0.9312), while XGBoost achieved the highest accuracy among ML models at 93.06%, with a 95% CI of (0.921, 0.941). Statistical tests validated the superiority of these models, with p-values of 5.48e-13 for RoBERTa and 3.41e-16 for XGBoost. These findings underscore the pivotal role of data augmentation and preprocessing in creating balanced datasets and enhancing the predictive capabilities of AI models for depression detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Contrastive Learning

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A retailer’s inventory model for deteriorating items under power pattern demand with shortages partially backlogged in both crisp and fuzzy environments

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International Journal of Industrial and Systems Engineering 2025年第1期49卷 96-118页

作者： Patra, Sourav Kumar Paikray, Susanta Kumar Tripathy, Rudra Mohan Department of Mathematics Veer Surendra Sai University of Technology Odisha Burla768018 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar752050 India

An inventory predicament can be resolved with numerous techniques, starting from the trial-and-error manner of mathematical and simulation methods. Mathematical methods always serve as powerful tools for minimising total inventory costs. In this paper, we have considered a retailer’s inventory problem in order to determine an optimal strategy that minimises the total inventory cost under various constraints. Here, the constraints include constant deterioration, power-pattern demand, permissible shortages, partial backlog, different inventory costs, and inherent imprecision of various expenses concerning the current scenario. Subsequently, we develop the mathematical model of the problem together with its solving policy in a crisp as well as fuzzy environments. Moreover, we provide several numerical illustrations to validate our findings. Finally, we present several managerial insights for inventory managers based on the sensitivity analysis of associated parameters. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Sensitivity analysis

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Watermarking System Using DWT and SVD

Watermarking System Using DWT and SVD

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Khudair, Fatima M. Hashim, Asaad N. Alsalhy, Mohammed Jameel Faculty of Computer Science and Mathematics University of Kufa Kufah Iraq National University of Science and Technology Thi-Qar Nasiriyah Iraq

ISBN: (纸本)9789819965434

Information hiding has garnered significant attention from researchers over the past two decades, due to its growing importance in securing visual applications. As a consequence, watermarking has become a focal point in numerous studies for its ability to protect sensitive data from unauthorized access, copying, manipulation, and infringement of copyrights or property rights. Watermarks can be applied to a wide range of mediums, including texts, documents, books, audio, video, and images. Various watermarking techniques exist, such as the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the discrete wavelet transform (DWT), the singular value decomposition (SVD), deep learning, and other more methods, each of which has its own set of benefits and drawbacks. In this paper, we propose a novel algorithm that combines the strengths of both SVD and DWT approaches to enhance watermarking performance. This innovative watermarking technique yields accurate results and has demonstrated exceptional performance metrics, as evidenced by signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) measurements. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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Oral Cancer detection using Histopathology Images 2

Oral Cancer detection using Histopathology Images

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Meyyappan, M. Verma, Aniket Kaushik, Aarush Sathyapriya, L. Suresh Anand, M. Shanmugam, Selvanayaki Kolandapalayam SRM Institute of Science and Technology Kattankulathur Chennai India Department of Mathematics and Computer Science Ashland University OH United States

ISBN: (纸本)9798350383867

To increase the chances of survival for the millions of individuals impacted by oral cancer (OC), early detection of the disease is essential. More than 177,384 people died from OC in 2018, and people in the low- to middle-class demographic are primarily affected. The histological examination is the antiquated method of detecting mouth cancer. We are utilizing Deep Learning algorithms to identify OC and automate the process. This paper suggests using histopathology photos to identify OC by building CNN-based models using a transfer learning (TL) technique, then assembling the models to get the best accuracy possible. DenseNet-201, Resnet-50, and EfficientNet-B3 will be used to train the TL models using histopathology pictures. When the three models are combined, the final model has an accuracy of 93.16%, which enables the model to be used in real time and is useful for categorizing images of OC. © 2024 IEEE.

关键词： Lung cancer

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Differentially processed optimized collaborative rich text editor

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Multimedia Tools and Applications 2025年第17期84卷 17189-17204页

作者： Jatana, Nishtha Singh, Mansehej Gupta, Charu Dhand, Geetika Malik, Shaily Dadheech, Pankaj Aneja, Nagender Aneja, Sandhya Maharaja Surajmal Institute of Technology New Delhi India Bhagwan Parshuram Institute of Technology Delhi India Swami Keshvanand Institute of Technology Management & Gramothan Rajasthan Jaipur India School of Digital Science Universiti Brunei Darussalam Bandar Seri Begawan Brunei Department of Computer Science Purdue University West LafayetteIN United States School of Computer Science and Mathematics Marist College PoughkeepsieNY United States

A collaborative real-time text editor is an application that allows multiple users to edit a document simultaneously and merge their contributions automatically. It can be made collaborative by implementing a conflict resolution algorithm either on the client side (in peer-to-peer collaboration) or on the server side (when using web sockets and a central server to monitor state changes). Although web sockets are ideal for real-time text editors, using multiple collaborative editors on one connection can create problems. This is because a single web connection cannot monitor which user is collaborating on which application state, leading to unnecessary network queries and data being delivered to the wrong state. To address this issue, the current solution is to open multiple web socket connections, with one web socket per collaboration application. However, this can add significant overhead proportional to the number of apps utilized. In this study, we demonstrate an algorithm that enables using a single web socket for multiple collaborative applications in a collaborative editor. Our method involves modifying the socket's code to track which application's shared state is being worked on and by whom. This allows for the simultaneous collaboration of multiple states in real-time, with infinite users, without opening a different socket for each application. Our optimized editor showed an efficiency improvement of over 96% in access time duration. This approach can be implemented in other collaborative editors and web applications with similar architecture to improve performance and eliminate issues arising from network overload. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： File editors

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Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 639-650页

作者： Liu, Kai You, Kang Gao, Pan Paul, Manoranjan Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Charles Sturt University School of Computing and Mathematics BathurstNSW2678 Australia

With the great progress of three-dimensional (3-D) sensing and acquisition technology, the volume of point cloud data has grown dramatically, which urges the development of efficient point cloud compression methods. In this article, we focus on the task of learned lossy point cloud attribute compression (PCAC). We propose an efficient attention-based method for lossy compression of point cloud attributes leveraging on an autoencoder architecture. Specifically, at the encoding side, we conduct multiple downsampling to best exploit the local attribute patterns, in which effective external cross attention (ECA) is devised to hierarchically aggregate features by intergrating attributes and geometry contexts. At the decoding side, the attributes of the point cloud are progressively reconstructed based on the multiscale representation and the zero-padding upsampling tactic. To the best of our knowledge, this is the first approach to introduce attention mechanism to point-based lossy PCAC task. We verify the compression efficiency of our model on various sequences, including human body frames, sparse objects, and large-scale point cloud scenes. Experiments show that our method achieves an average improvement of 1.15 and 2.13 dB in Bjontegaard delta (BD)-peak signal-to-noise ratio (BD-PSNR) of Y channel and YUV channel, respectively, when comparing with the state-of-the-art point-based method deep-PCAC. Codes of this article are available at https://***/I2-Multimedia-Lab/Att2CPC. © 2024 IEEE.

关键词： HTTP

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Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

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Materials Today: Proceedings 2023年 72卷 958-965页

作者： Sinha, Anurag Garg, Namit Singh, N.K. Kumar, Biresh Banerjee, Pallab Verma, Achyut Kumar Department of Computer Science IGNOU Delhi India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Computer Science and Engineering Amity University Jharkhand Ranchi Jharkhand India Department of Chemical Engineering Indian Institute of Technology Kanpur India

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated. © 2023

关键词： Reinforcement learning

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Recommender System to Identify Drug-Target Associations Using Matrix Factorization 9

Recommender System to Identify Drug-Target Associations Usin...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Phuangmalai, Sornsawan Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand Center Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9798350318241

Recommender systems, widely used in information filtering systems, demonstrate efficacy in suggesting novel items to users. In this study, we propose the application of a recommender system to predict new drug-target associations. Here, drugs are represented as users, while protein targets are treated as items within the recommender system. However, due to sparse data in a matrix of drugs and protein targets, matrix factorization (MF) techniques are employed to decompose the extensive matrix into smaller matrices. We identify non-negative matrix factorization (NMF) and singular value decomposition (SVD) as top-performing models for this task. Subsequently, we integrated them into the traditional recommender system, encompassing both drug-based collaborative filtering and target-based collaborative filtering, to identify new drug-target associations aligned with observed interactions between drugs and target proteins. Finally, we evaluated the performance of our developed recommender system with matrix factorization for drug-target associations and compared the results with those obtained from a recommender system without matrix factorization. © 2024 IEEE.

关键词： Collaborative filtering

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