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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Airborne Open Digital Drive System Based on Permanent Magnet Brushless Motor 19th

An Airborne Open Digital Drive System Based on Permanent Mag...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Liu, Shaolong Feng, Wei Xu, Yebin Liu, Li Yang, Qifan Aviation Industry Corporation of China Xi’an Aviation Computing Technology Institute Xi’an China

ISBN: (纸本)9789819613823

To improve the reliability and anti-interference capability of the actuator drive system onboard aircraft, this paper proposes a novel open, full-digital, high-integrity drive system design method based on permanent magnet brushless motors. The drive system is equipped with multiple protection circuits at the power input stage to deal with and suppress various non-normal power inputs such as overvoltage, under-voltage, overcurrent, and lightning strikes. At the bus input stage, an improved protocol-based, question-and-answer open communication CAN bus is adopted to improve the stability of external communication. In the digital control stage, a dual-channel, full-digital, dual closed-loop control method is adopted to achieve high integrity of the drive system. In the execution stage, a permanent magnet brushless motor is used as the load movement actuator. Finally, a hardware prototype was built for experimental analysis, and the power input protection function, waveform of the motor control link, and control accuracy of the drive system over the full range of the actuator were recorded and calculated. The experimental results show that the drive system has excellent performance, high control precision, and can be used as a reference for the design of actuator drive systems onboard aircraft. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(Information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Label-specific multi-label text classification based on dynamic graph convolutional networks

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Soft computing 2025年第3期29卷 1897-1909页

作者： Yan, Yaoyao Liu, Fang‘ai Liu, Kenan Xu, Weizhi Zhuang, Xuqiang Information Science and Engineering School Shandong Normal University Jinan250358 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Multi-label text classification is a key task in natural language processing, aiming to assign each text to multiple predefined categories simultaneously. Existing neural network models usually learn the same text representation for different labels, which limits the effectiveness of the models in capturing deep semantics and distinguishing between similar labels;moreover, these models tend to ignore inter-label correlation, leading to loss of information. To overcome these limitations, we propose a novel label-specific dynamic graph convolutional network (LDGCN). This network combines convolutional operations and BiLSTM to model text sequences and obtains label-specific text representations through a label attention mechanism. In addition, LDGCN improves the dynamic graph convolutional network by utilizing statistical label co-occurrence and label reconstruction maps to effectively capture inter-label dependencies and adaptive interactions between label-specific semantic components. Extensive experiments on the RCV1, AAPD, and EUR-Lex datasets show that our model achieves 96.92%, 86.30%, and 81.42% on the P@1 metrics, respectively, and demonstrates a significant advantage in dealing with tail labels. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Semantics

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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MFC based Smart Irrigation System using IoT and Machine Learning 6

MFC based Smart Irrigation System using IoT and Machine Lear...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Chaitanya, Manne Sai Uday Kiran Reddy, B. Sreekumar, K. Amaran, Sibi Department of Computing Technologies Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing SRM Institue of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798331522667

This research study proposes a novel Smart Irrigation System that integrates Microbial Fuel Cells (MFCs) with IoT and Machine Learning for sustainable agricultural practices. The system uses MFCs to generate electricity from soil microorganisms, providing a renewable energy source for irrigation. IoT sensors monitor real-time environmental parameters such as temperature, humidity, and soil moisture, and transmit data to cloud platforms for analysis. Machine learning algorithms are used to process the historical data, weather forecast, and sensor information in real-time to predict the irrigation requirement and optimize water usage. Renewable energy generation, IoT-based monitoring, and machine learning-driven decision support strategy are integrated in this system to improve water efficiency, reduce energy consumption, and support sustainable agricultural practice. © 2025 IEEE.

关键词： Smart agriculture

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Deploy a Secure Chat Application for Log Monitoring in Kubernetes 3

Deploy a Secure Chat Application for Log Monitoring in Kuber...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dinesh, S. Nivedha, R. Mahalakshmi, S. Balasaigayathri, B. Department Of CSE Cyber Security Sathyabama Institute of Science and Technology Chennai India School of computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

This paper presents the development and deployment of a secure chat application in a Kubernetes environment for real-time log monitoring and communication. The research addresses the challenges of managing and aggregating logs in a distributed, containerized environment, specifically within Kubernetes clusters. Traditional logging solutions often struggle with providing real-time collaborative troubleshooting, centralized log aggregation, and robust security. The proposed system integrates Kubernetes-native features, along with Fluentd and the ELK stack (Elasticsearch, Logstash, Kibana), to centralize log management and provide a unified interface for real-time communication through WebSockets. The application is designed to ensure high scalability, fault tolerance, and security by implementing TLS encryption and Role-Based Access Control (RBAC). The outcome is a scalable, secure, and efficient log monitoring solution that improves operational efficiency, enhances system reliability, and fosters collaborative debugging, making it a valuable tool for developers and administrators in Kubernetes-based environments. © 2025 IEEE.

关键词： Fault tolerance

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COMET: Towards Practical W4A4KV4 LLMs Serving 25

COMET: Towards Practical W4A4KV4 LLMs Serving

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30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Liu, Lian Cheng, Long Ren, Haimeng Xu, Zhaohui Pan, Yudong Wang, Mengdi Li, Xiaowei Han, Yinhe Wang, Ying Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China North China Electric Power University Beijing China ShanghaiTech University Shanghai China Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798400710797

Quantization is a widely-used compression technology to reduce the overhead of serving large language models (LLMs) on terminal devices and in cloud data centers. However, prevalent quantization methods, such as 8-bit weight-activation or 4-bit weight-only quantization, achieve limited performance improvements due to poor support for low-precision (e.g., 4-bit) activation. This work, for the first time, realizes practical W4A4KV4 serving for LLMs, fully utilizing the INT4 tensor cores on modern GPUs and reducing the memory bottleneck caused by the KV cache. Specifically, we propose a novel fine-grained mixed-precision quantization algorithm (FMPQ) that compresses most activations into 4-bit with negligible accuracy loss. To support mixed-precision matrix multiplication for W4A4 and W4A8, we develop a highly optimized W4Ax kernel. Our approach introduces a novel mixed-precision data layout to facilitate access and fast dequantization for activation and weight tensors, utilizing the GPU's software pipeline to hide the overhead of data loading and conversion. Additionally, we propose fine-grained streaming multiprocessor (SM) scheduling to achieve load balance across different SMs. We integrate the optimized W4Ax kernel into our inference framework, COMET, and provide efficient management to support popular LLMs such as LLaMA-3-70B. Extensive evaluations demonstrate that, when running LLaMA family models on a single A100-80G-SMX4, COMET achieves a kernel-level speedup of 2.88x over cuBLAS and a 2.02x throughput improvement compared to TensorRT-LLM from an end-to-end framework perspective. © 2025 ACM.

关键词： Tensors

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VisionAid: Enhancing Accessibility for the Visually Impaired with YOLO and gTTS

VisionAid: Enhancing Accessibility for the Visually Impaired...

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

作者： Lakshmi Raghupathy, Bhagya Nithyashri, J. Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331521394

Daily environment navigating and accessing visual information are critical problems for people with vision disabilities. To reduce this discrepancy VisionAid, an assistive application was introduced to make visually challenged person have better accessibility via integration of YOLO for real-time object detection and gTTS which is library used for text-to-speech conversion. It recognizes objects, speaks text aloud and promotes good navigation skills that improve mobility and independence. VisionAid augments vision loss with modern technologies like GPS and SLAM for navigation, rather than traditional aids. This paper presents VisionAid as an end-to-end solution, fills the gaps in existing models and brings more autonomy to its users. © 2025 IEEE.

关键词： Object detection

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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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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

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

ISBN: (纸本)9798331508685

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. © 2025 IEEE.

关键词： Lung cancer

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