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Benchmarking model chemistry composite calculations for vertical ionization potential of molecular systems

Chinese Chemical Letters 2025年第2期36卷 508-511页

作者： Bingwei Wang Yihong Ding Xiao Tian Key Laboratory of Carbon Materials of Zhejiang Province Wenzhou Key Lab of Advanced Energy Storage and ConversionZhejiang Province Key Lab of Leather EngineeringCollege of Chemistry and Materials EngineeringWenzhou UniversityWenzhou 325035China School of Mathematics and Science Hebei GEO UniversityShijiazhuang 050031China Laboratory of Theoretical and Computational Chemistry Institute of Theoretical ChemistryJilin UniversityChangchun 130023China

In chemical science,the vertical ionization potential(VIP)is a crucial metric for understanding the electronegativity,hardness and softness of chemical material systems as well as the electronic structure and stability of *** since the last century,the model chemistry composite methods have witnessed tremendous developments in computing the thermodynamic properties as well as the barrier ***,their performance in realm of the vertical electron processes of molecular systems has been rarely *** this study,we for the first time benchmarked the model chemistry composite methods(e.g.,CBS-QB3,G4 and W1BD)in comparison with the commonly used Koopmans's theorem(KT),electron propagator theory(e.g.,OVGF,D2,P3 and P3+)and CCSD(T)methods in calculating the VIP for up to 613 molecular systems with available experimental *** large-scale test calculations strongly showed that the CBS-QB3 model chemistry composite technique can be well recommended to calculate VIP from the perspectives of accuracy,economy and ***,the VIP values of up to 7 molecules were identified to have the absolute errors of larger than 0.3 e V at all calculation levels,which have strong hints that their VIP experimental values should be re-investigated.

关键词： Model chemistry Vertical ionization potential Composite methods Molecular systems CBS-QB3

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Pairing-Free Blockchain-Assisted Certificateless Aggregation Signcryption Scheme for VANETs

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IEEE Internet of Things Journal 2025年第11期12卷 15545-15557页

作者： Wang, Yahui Peng, Cong Jia, Xiaoying Wen, Jiaming Zhang, Yuanyuan Xinyang Normal University School of Computer and Information Technology School of Software Xinyang464000 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China South-Central Minzu University School of Mathematics and Statistics Wuhan430074 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2522 Australia Hubei University of Technology School of computer science Wuhan430072 China

Smart vehicle applications play a crucial role in intelligent transportation systems, enabling sensor-equipped vehicles to establish dynamic networks for efficient collection, sharing, and aggregation. This significantly enhances road security and efficiency by transmitting crucial information to traffic authorities. However, partial research on certificateless aggregation signcryption (CLAS) scheme reveals an intriguing phenomenon where each proposed scheme consistently exhibits numerous security vulnerabilities, particularly susceptible to public key replacement attacks. To address these challenges, this paper proposes a blockchain-assisted certificateless aggregation sign-cryption scheme (BACLAS), leveraging blockchain technology to securely store users' public key on a distributed ledger and prevent public key replacement attacks effectively. Furthermore, it is a provably secure communication scheme for real-world Vehicle-to-Infrastructure (V2I) communication while addressing practical security concerns. The BACLAS scheme ensures security in terms of existential unforgeability against adaptive chosen message attacks (EUF-CMA) and indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) based on the hardness assumption of the elliptic curve discrete logarithm problem and computational Diffie-Hellman problem in the random oracle model. The proposed scheme effectively reduces computational costs and time consumption, resulting in a significant reduction of the computation burden ranging from 50.02% to 88.28% compared to other competitive schemes. Moreover, it successfully addresses the key-escrow problem, thereby achieving enhanced security properties. © 2014 IEEE.

关键词： Vehicular ad hoc networks

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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： T. Illakiya B. Vidhya S. Prince Chelladurai S. Hariprasad Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Mild Cognitive Impairment (MCI) refers to a neurological disorder marked by observable changes in cognitive decline. MCI signifies an intermediate phase linking normal aging and Alzheimer's Disease (AD). It is typically categorized into two types: Early MCI (EMCI) and Late MCI (LMCI), representing varying levels of cognitive impairment. An effective classification between EMCI and LMCI is vital, as it allows timely interventions that can potentially slow the progression of AD, improving patient outcomes and quality of life. Identifying the subtle features and patterns in EMCI and LMCI is essential for early diagnosis and effective intervention. This work proposes a novel Deep Learning (DL) network integrating EfficientNet-B0, a Global Context Network (GCN), and Neighborhood Attention blocks. The EfficientNet-B0 acts as the foundation for feature extraction in the proposed model. It uses its efficient architecture and pre-trained capabilities to capture meaningful patterns. To further enhance feature representation, a Global Context Network (GCN) is employed to capture long-range dependencies and global semantic information. Additionally, a Neighborhood Attention Module is used to refine local features by focusing on important regions in the MRI scans. The proposed framework balances global semantic understanding and localized feature extraction, resulting in improved classification performance. With systematic training and evaluation, the model achieves an accuracy of 95.3%, outperforming existing approaches and providing a better solution for neuroimaging-based MCI subtype classification.

关键词： Deep learning Training Accuracy Systematics Magnetic resonance imaging Visual analytics Semantics Feature extraction Brain modeling Convolutional neural networks

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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Illakiya, T. Vidhya, B. Chelladurai, S. Prince Hariprasad, S. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331521394

关键词： Convolutional neural networks

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Exploring Discrete-Time Epidemic Behavior Based on Saturated Incidence Rate and Multiple Transmission Pathways

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Qualitative Theory of Dynamical Systems 2025年第3期24卷 1-30页

作者： Deolia, Preeti Sharma, Vijay Shankar Singh, Anuraj Department of Engineering Sciences ABV-Indian Institute of Information Technology and Management Gwalior India Department of Engineering Mathematics and Computing Madhav Institute of Technology & Science (MITS) Gwalior India

The viral shedding is crucial for understanding the transmission of infectious diseases via contaminated surfaces. In this work, a discrete-time SIR-type mathematical model incorporating pathogen shedding is investigated. The basic reproduction number ( $$R_{0}$$ ) is used to determine the conditions for the local stability of the fixed points of the discrete-time model. The existence of co-dimension one bifurcations particularly, transcritical (BP) and period-doubling (PD) bifurcations, along with co-dimension two bifurcations namely, generalized period doubling (GPD), and limit point period doubling (LPPD) bifurcations are discussed. The presence of these bifurcations is detected using algebraic criterion methods. These methods are based on the properties of the coefficients of the characteristic polynomial instead of the eigenvalues of the Jacobian matrix. The sensitivity indices of $$R_{0}$$ are calculated via the normalized forward sensitivity method with respect to significant parameters. The performed numerical simulation supports the analytical findings of the model. The existence of several bifurcations in the discrete-time epidemic model reflects a situation where a slight adjustment in some specific parameters can significantly impact the model outcomes.

关键词： Discrete dynamical system Basic reproduction number Codimension-2 bifurcation Period-doubling bifurcation Sensitivity analysis.

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Computer Aided Diagnosis Multi-model System using Late Fuison and Ensemble Learning

Computer Aided Diagnosis Multi-model System using Late Fuiso...

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Pervasive computational Technologies (ICPCT), International Conference on

作者： M.Kani Priya Gunabhiram Aruru Nikhil Vanam M. Suganiya M. Salomi Department Of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Lung Cancer and brain tumors are the well-known causes of cancer deaths universal. Therefore, appropriate and accurate diagnosis is an important issue that affects better and more reliable treatment and the patient's chance of survival. Present diagnostic strategies are mostly based on a single piece of information. This often leads to missing necessary information, which can lead to Misidentification of condition or delay in timely treatment. This study presents a novel Computer-Aided-Diagnosis system designed to enhance the detection of lung cancer and brain tumors by integrating multimodal data, including medical images (CT and MRI scans) and clinical information. The proposed approach employs late fusion and ensemble learning techniques, utilizing CNN for processing CT images, VGG19 for analyzing MRI scans, and Random Forest classifiers for evaluating clinical data. By separately training these models, the system capitalizes on the distinct characteristics of each data modality, overcoming the limitations of traditional CAD systems that typically rely on a single data source. This comprehensive analysis not only improves diagnostic accuracy but also provides reliable predictions, ultimately facilitating better clinical decision-making for lung cancer and brain tumor detection.

关键词： Solid modeling Computed tomography Magnetic resonance imaging Decision making Lung cancer Brain tumors Brain modeling Data models Ensemble learning Medical diagnostic imaging

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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