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Advancement in Technology (ICONAT), International Conference for

作者： Vishnu Kurnala Department of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

This paper introduces a novel security framework to fortify healthcare data against cyber threats, incorporating blockchain technology and ledger tokenization within an integrated Intrusion Detection and Protection System (IDPS). Faced with escalating cybersecurity challenges, our approach merges traditional protection mechanisms with cutting-edge technologies to offer unparalleled data integrity and security. By leveraging blockchain for immutable data trails and enhancing IDPS with advanced machine learning techniques, our solution provides a robust defense against a range of cyber threats, including ransomware and data breaches. Extensive testing in simulated environments confirms the effectiveness of our system, suggesting it as a viable blueprint for future developments in healthcare cybersecurity. This study not only demonstrates the application of blockchain in enhancing data security but also sets the stage for further innovative practices in protecting sensitive healthcare information.

关键词： Firewalls (computing) Data integrity Intrusion detection Medical services Machine learning Tokenization Blockchains Ransomware Protection Testing

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Rainfall Forecasting Using Deep Learning and Cloud Image Analysis

Rainfall Forecasting Using Deep Learning and Cloud Image Ana...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： K. RamaKrishna Reddy V. Sravya Reddy V. Sriram Sathvik Shaik Mahaboob Subhani Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study introduces a new approach to rainfall prediction using deep learning and Convolutional Neural Networks (CNNs) for analyzing cloud image data sourced from diverse platforms. Traditional methods often struggle to capture precipitation patterns' complexity, necessitating advanced technologies like CNNs. The methodology emphasizes meticulous data preprocessing, including multiple discriminations, ratio adjustment, and augmentation, optimizing input data for CNNs to enhance quality, robustness, and predictive model effectiveness. A binary classification component enhances prediction accuracy and provides valuable insights into rainfall occurrence's binary nature. Using the multi-class weather dataset (MWD), this study explores weather image classification, examining dataset structure and showcasing sample images for each weather type. Two models with distinct architectures are trained from scratch, alongside a transfer learning model utilizing the VGG 16 pre-trained model. Result demonstrate promising performance across all models on the test dataset, highlighting their ability to accurately classify weather types. This study contributes to advancing rainfall prediction methodologies, offering insights into CNN application for weather analysis and forecasting, addressing critical needs in various sectors reliant on accurate weather predictions.

关键词： Deep learning Rain Accuracy Transfer learning Predictive models Real-time systems Convolutional neural networks Forecasting Meteorology Image classification

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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science China(Information sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong Cai Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng Dai Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(mlLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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Deep Sentiment: “An Effective Sentiment Analysis of Twitter Data using BERT+GRU Hybrid Model”

Deep Sentiment: “An Effective Sentiment Analysis of Twitter...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Sudhakar T. Jatin Pratap Singh M. Thanuj B. Praveen Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Sentiment Analysis (SA) has been an active area of research for over a decade. It involves an in-depth study of opinions, sentiments, and subjectivity expressed in text. Among various platforms, Twitter is one of the most popular microblogging services and serves as a significant source for sentiment analysis and data exploration. Organizations globally utilize SA on Twitter data to gain insights with applications across various domains. However, while SA is effective for understanding public opinion, bias in tweets can distort analysis, leading to inaccurate results and potentially influencing users to make flawed decisions. Such biased tweets may originate from both authentic but biased individuals and automated social bots that disseminate skewed opinions on specific topics. To address this challenge, this research proposes a statistical model designed to detect users and bots responsible for spreading biased content on Twitter. Using an annotated Twitter dataset, the study evaluates sentiment analysis results with and without biased tweets, analysing the impact of biased users at both micro and macro levels. Experimental results demonstrate that the proposed approach effectively distinguishes biased users and bots from genuine users, thereby enhancing the accuracy of sentiment analysis.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Computational modeling Blogs Organizations Control systems Data models Data mining

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ChemDFM-X: towards large multimodal model for chemistry

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science China(Information sciences) 2024年第12期67卷 99-100页

作者： Zihan ZHAO Bo CHEN Jingpiao LI Lu CHEN Liyang WEN Pengyu WANG Zichen ZHU Danyang ZHANG Yansi LI Zhongyang Dai Xin CHEN Kai YU X-LANCE Lab Department of Computer Science and EngineeringMoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Suzhou Laboratory

Chemistry, as a naturally multimodal discipline, plays a crucial role in various vital fields such as pharmaceutical research and material manufacturing. Therefore, research on artificial intelligence(ai) for chemistry has garnered increasing attention. Despite the rapid development, most of the chemical ai models today mainly focus on single tasks with unimodal input [1].

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ai-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an ai-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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How Does Software Prefetching Work on GPU Query Processing? 20

How Does Software Prefetching Work on GPU Query Processing?

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20th International Workshop on Data Management on New Hardware, DaMoN 2024

作者： Deng, Yangshen Chen, Shiwen Hong, Zhaoyang Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology AlayaDB Ai Guangdong Shenzhen China

ISBN: (纸本)9798400706677

Improving the performance of GPU query processing is a well-studied problem in database community. However, its performance is still unsatisfactory due to the low utilization of GPU memory bandwidth. In the literature, employing software prefetching techniques to improve the bandwidth utilization is a common practice in CPU database as it overlaps computation cost and memory access latency. However, it was ignored by GPU database even though the software prefetching ability has been provided by modern GPU architecture (i.e., from NVIDIA Ampere). In order to investigate the effectiveness of software prefetching techniques on GPU query processing, we implement four software prefetching algorithms on GPU, i.e., Group Prefetch (GP), Software-Pipelined Prefetch (SPP), Asynchronous Memory Access Chaining (AMAC) and Interleaved Multi-Vectorizing (IMV) in the work. We then adapt them on hash join probe and BTree search tasks with a suite of optimizations. Last, we conduct comprehensive experiments and evaluate the performance of them. The results confirm the superiority of software prefetching techniques on GPU query processing. Specifically, they can achieve up to 1.19X speedup on hash join probe and 1.31X speedup on BTree search when compared with the implementations without software prefetching. © 2024 ACM.

关键词： Graphics processing unit

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Proactive Purity: Predicting Water Quality with Precision

Proactive Purity: Predicting Water Quality with Precision

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： K. Sandhya G. Ashitha B. Sharath Kumar B. Varun Kumar T. Bharadwaj Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Water pollution, driven by industrialization and urbanization, threatens public health, aquatic ecosystems, and livelihoods worldwide. Rivers, being the primary source of water for drinking, agriculture, and industry, are highly vulnerable to contamination from untreated sewage, industrial discharge, and agricultural runoff. Traditional water quality monitoring methods are expensive, time-consuming, and lack real-time *** study proposes a technology-driven water quality monitoring system capable of processing large data volumes in real-time, identifying pollution patterns, and supporting timely interventions. By integrating advanced monitoring techniques, the system enhances decision-making for water management, ensuring sustainability and ecological balance. The research highlights the critical role of technology in addressing global water pollution balance, further underlining the very important linkage between technology and *** data volumes from source water can be processed by this system, identifying patterns and trends that indicate fluctuation in water quality. this system is particularly beneficial in areas that experience seasonal and man-made causes of change in water quality. Besides its technological achievements, this project is a giant leap toward the management of water *** technology provides authority to decision makers. who together with environmentalists and enables easy accessibility for various tasks to make sensible decisions for drinking water supply as well as irrigation systems and pollution prevention measures for water sources. measures. It has great importance as it combines science and technology in saving vital natural resources and maintaining long-term ecological balance by tackling one of the most urgent worldwide environmental issues.

关键词： Machine learning algorithms Water quality Prediction algorithms Market research Water pollution Real-time systems Pollution measurement Public healthcare Water resources Monitoring

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PaySafe Al: Intelligent Fraud Detection for UPI Transactions using Machine Learning

PaySafe Al: Intelligent Fraud Detection for UPI Transactions...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Maddela Ajay Mukkerla Saiteja Baddula Omkar Yadav Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

In the financial world, it is impoartant to have a sound understand of systems against fraud, as they help detect activities such as anomalous and malicious transactions, through the analysis of activity engagement. Decision Tree are the recent form of deep learning models which, in more recent years, have shown advanced capability in the identification of fraudulent transactions, however, sensitivity and accuracy when implemented in real time systems remain an issue. In this research, we provide a new idea of fraud detection of UPI transactions that utilizes a Decision Tree based sequence modeling framework. The model focuses on normalization sets and typical cardholder activities to effectively learn how to detect anomalies in UPI transactions. It then analyzes the sequences of a person’s activities and adds on the probabilities, to then classify if the transaction is fraudulent or not. Several measures are established, however, in order to aid in minimizing false positives, because the aim is to ensure valid transactions are still processed. The experimental results support the proposed fraud detection system’s potential usefulness in monitoring actual UPI transactions and it also shows how it outperforms current systems in related studies. Our model facilitates the UPI transaction environment equally and addresses the fine balance in detection efficiency, accuracy and transaction speed by proposing the integration of deep learning techniques.

关键词： Support vector machines Deep learning Logistic regression Accuracy Sensitivity Predictive models Fraud Decision trees Security Standards

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