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National Conference on Communications (NCC)

作者： Cheeranjiv Pandey Sanidhya Gupta Rimon Ranjit Das Ankur Raina Department of Physics Indian Institute of Technology Ropar Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Department of Physics Amity Institute of Applied Sciences Amity University

Classical network coding uses encoding of information over networks to achieve secure and high throughput communication with low latency. Its quantum analog, namely Quantum Network Coding (QNC) is a promising protocol to achieve quantum communication over quantum networks. In this paper, we propose a method for establishing QNC in Measurement-Based Quantum Computing (MBQC), which is a universal quantum computational model. We demonstrate that the problem of multiple unicast or multiple multicast of distinct computational basis states over a quantum network can be solved by simulating existing classical networks, with the CNOT implementation in MBQC as a basis for transmitting and encoding of states. We also show how it can be further extended to simultaneously distribute the Bell pairs and Greenberger–Horne–Zeilinger (GHZ) states required for quantum communication between distant transmitter and corresponding receiver nodes of a network.

关键词： Protocols Unicast Transmitters Quantum entanglement Receivers Network coding Throughput

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AutoML Framework for Physical Activities Recognition

AutoML Framework for Physical Activities Recognition

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Vladislav Kovalevsky Radhakrishnan Delhibabu Nataly Zhukova St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The rise of measuring, computing, and storage capabilities in modern information systems has led to vast amounts of data in various fields of human activities. Various algorithms for machine learning have been developed to get valuable insights from this raw data. Tuning the hyperparameters of each algorithm is necessary. Finding the right algorithm and manually tuning its hyperparameters for a given task can be very time-consuming. The methods for automating this search are within the scope of AutoML (automated machine learning). The wide use of wearable devices such as smartwatches and fitness trackers enables the gathering of various information about the physical activities of the wearer. This information, in turn, can be used to produce useful insights, such as recognizing activities being performed. AutoML can automate the process of building models for this task, but the performance of the resulting models can be very different depending on the AutoML library that performed the task. In this work, we develop an AutoML framework that unites several commonly used AutoML libraries and enables the use of common constraints and metrics. The developed framework is then used to build models for the task of human activity recognition by processing a dataset containing information collected from smart devices worn by users.

关键词： Performance evaluation Water Accuracy Machine learning algorithms Search problems Libraries Data models

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Design of a Secure Machine Learning-Based Malware Detection and Analysis Scheme

Design of a Secure Machine Learning-Based Malware Detection ...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： Machine learning

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Rag2Mol: Structure-Based Drug Design Based on Retrieval Augmented Generation 29th

Rag2Mol: Structure-Based Drug Design Based on Retrieval Aug...

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29th International Conference on research in Computational Molecular Biology, RECOMB 2025

作者： Zhang, Peidong Peng, Xingang Han, Rong Chen, Ting Ma, Jianzhu Department of Computer Science and Technology Tsinghua University Beijing China School of Intelligence Science and Technology Peking University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Institute for AI Industry Research Tsinghua University Beijing China

ISBN: (纸本)9783031902512

Artificial intelligence (AI) has significantly advanced drug discovery, yet identifying optimal hit and lead compounds remains challenging. Structure-based drug design (SBDD) has emerged as a promising paradigm, but the inherent data biases and ignorance of synthetic accessibility render SBDD models disconnected from practical drug discovery. We introduce Rag2Mol-G and Rag2Mol-R, two retrieval-augmented generation (RAG) methods for discovering small molecules fitting 3D pockets. These two methods involve searching for similar small molecules that are purchasable in the database based on the generated ones, or creating new molecules from those in the database that can fit into a 3D pocket. Our experiments indicate that Rag2Mol consistently generates candidates with high binding affinities and drug-likeness. Rag2Mol-R, in particular, offers broader chemical landscape coverage and superior targeting compared to advanced virtual screening models. Both methods successfully identified inhibitors for the challenging target PTPN2, previously deemed undruggable. Our extensible framework can integrate various SBDD approaches, and the code is available at: https://***/CQ-zhang-2016/Rag2Mol. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drug discovery Retrieval-augmented generation Structure-based drug design

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Permanent Magnet Synchronous Motor Control and Implementation of this Control into the Microprocessor and its Realization

Permanent Magnet Synchronous Motor Control and Implementatio...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Rathnakar G Harendra Singh Negi G. Shashibhushan Praful V. Nandankar Daxa Vekariya Ramya Maranan Department of Mechanical Engineering JSS Science and Technology University Mysuru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electronics and Communication Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Karnataka India Department of Electrical Engineering Government College of Nagpur Maharashtra India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Traditional methods of motor control, such as scalar control or field-oriented control (FOC), may struggle to provide the same level of precision and responsiveness as vector control. This work explores the implementation of vector control in the STM32F302R8 microprocessor for precise regulation of speed and torque in electric motors, commonly utilized in industrial automation and electric vehicles. Prior to vector control implementation, the microprocessor interfaces with essential components: the inverter for DC to AC power conversion, the control module housing control algorithms, and the resolver for rotor position feedback. The process involves programming and testing to accurately evaluate the rotor's rotation angle, crucial for effective vector control by ensuring correct voltage application to motor windings. Upon successful implementation and testing, the vector control system demonstrates functionality in regulating motor speed responsively to desired speed changes.

关键词： Torque Microprocessors Windings Rotors Inverters Vectors Voltage control

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A Smoothed Bregman Proximal Gradient Algorithm for Decentralized Nonconvex Optimization

A Smoothed Bregman Proximal Gradient Algorithm for Decentral...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenqiang Pu Jiawei Zhang Rui Zhou Xiao Fu Mingyi Hong Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China LIDS Massachusetts Institute of Technology USA School of Electrical Engineering and Computer Science Oregon State University Corvallis USA Department of Electrical and Computer Engineering University of Minnesota MN USA

Decentralized computation has received considerable research interest lately, due to its wide applications in information processing systems. However, one key requirement to establish convergence for almost all decentralized algorithms, for convex and non-convex problems alike, is that the loss function has Lipschitz-continuous gradient (LipGrad). This is a strong assumption, which does not hold for many practical problems, such as matrix/tensor factorization, neural network training, etc. On the contrary, in the centralized setting, one can utilize techniques such as the Bregman proximal gradient (BPG) method to deal with the lack of LipGrad. This work fills the gap between centralized and decentralized cases by developing a novel smoothed decentralized BPG algorithm to deal with a class of nonconvex decentralized problem, where the local problems do not have LipGrad objective functions. By leveraging the recent notion of relative smoothness and primal-dual error bounds, we show that the proposed algorithm achieves a certain ε-stationary solution by using $\mathcal{O}\left( {{\varepsilon ^{ - 2}}} \right)$ iterations, matching the rate of the centralized Bregman proximal gradient method. To our knowledge, this is the first decentralized algorithm that matches the centralized convergence rate bounds under the class of considered problems. Our numerical results on the decentralized quadratic regression example demonstrate the effectiveness of proposed algorithm.

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Optimizing Wireless Sensor Network Routing Through Memetic Algorithms: Enhancing Energy Efficiency and Data Reliability

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Procedia computer science 2023年 230卷 150-157页

作者： Aishwarya D Saranya S Sathiya A Samuel Manoharan J Asst. Professor/Dept. of Computer Science and Engineering School of Computing Sathyabama Institue of Science and Technology Chennai India Asst. Professor (SS)/ Dept. of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Research Scholar/Dept. of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur India Professsor/Dept. of Electronics and Communication Engineering Sir Issac Newton College of Engineering and Technology Nagapattinam India

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods.

关键词： Wireless Sensor Networks Energy Efficient Routing Memetic Computing Evolutionary algorihtms

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Deformable Cross-Attention Trnsformer for Wekly Aligned RGB-T Pedestrin Detection

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IEEE Transactions on Multimedia 2025年

作者： Hu, Yu Chen, Xiaobo Wang, Sheng Liu, Luyang Shi, Hengyang Fan, Lihong Tian, Jing Liang, Jun Jiangsu University Automative Engineering Research Institute Zhenjiang212013 China Shandong Technology and Business University School of Computer Science and Technology Yantai264005 China People's Public Security University of China School of Transportation Management Beijing100038 China

Pedestrian detection plays a crucial role in autonomous driving systems. To ensure reliable and effective detection in challenging conditions, researchers have proposed RGB-T (RGB-thermal) detectors that integrate thermal images with color images for more complementary feature representations. However, existing methods face challenges in capturing the spatial and geometric correlations between different modalities, as well as in assuming perfect synchronization of the two modalities, which is unrealistic in real-world scenarios. In response to these challenges, we present a new deformableattention-based approach for weakly aligned RGB-T pedestrian detection. The proposed method uses a dual-branch crossattention mechanism to capture the inherent spatial and geometric correlations between color and thermal images. Furthermore, it incorporates positional information for each image pixel into the sampling offset generation to enhance robustness in scenarios where modalities are not precisely aligned or registered. To reduce computational complexity, we introduce a local attention mechanism that samples only a small set of keys and values within a limited region in the feature maps for each query. Extensive experiments and ablation studies conducted on multiple public datasets confirm the effectiveness of the proposed framework. Our code will be released at https://***/jiongger/DeformCAT. © 2025 IEEE.

关键词： Image enhancement

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A Review on the Applications of Natural Language Processing in Healthcare

A Review on the Applications of Natural Language Processing ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Mohan K. Warbhe Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards.

关键词： Sentiment analysis Ethics Data privacy Virtual assistants Semantics Natural language generation Medical services Natural language processing Medical diagnostic imaging Interoperability

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AI-Driven Threat Detection: Implementing Multi-Layer Security Networks in Cloud Environments

AI-Driven Threat Detection: Implementing Multi-Layer Securit...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： S. Surya D. Santhakumar Anuj Tyagi Kiran Onapakala V B Thurai Raaj N. Krishna Kumar Sri Ramachandra Faculty of Engineering & Technology(SRET) Sri Ramachandra Institute of Higher Education & Research Chennai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu Sr. Site Reliability Eng RingCentral Inc United States PacificSource Health Plans Portland Oregon USA Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of CSA University Institute of Computer Science and Applications Guru Nanak University Hyderabad Telangana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

AI-driven threat detection has become a vital tool for security maintenance as cloud environments become more and more integrated into digital infrastructures. In order to identify and reduce risks in cloud-based systems, this study investigates the deployment of multi-layer security networks utilising cutting-edge machine learning (ML) and deep learning (DL) models. We created a hybrid training strategy that blends historical and synthetic data for improved relevance in contemporary cloud environments by utilising datasets like KDD Cup 99 and CSECIC-IDS2018 in addition to simulated cloud network interactions. ML models such as Random Forest and Logistic Regression were used to create baseline threat detection, while Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks were used to accomplish deeper pattern recognition. The effectiveness of DL models in proactively protecting cloud infrastructures against changing cyber threats was demonstrated by thorough performance evaluations based on accuracy, precision, recall, F1 Score, and AUC-ROC. These evaluations also demonstrated the models’ better flexibility in dynamic cloud environments.

关键词： Ethics Technological innovation Reviews Cloud computing security Market research Threat assessment Security Artificial intelligence Long short term memory Standards

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