检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Ramesh, Dhanush Babu Sridhar, Rishika Iytha Upadhyaya, Pulakesh Kamaleswaran, Rishikesan Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States

Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung-related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosing lung diseases. While various segmentation methods exist, this study primarily focuses on META’s latest Segment Anything Model (SAM). SAM is an Artificial Intelligence (AI) model designed to segment objects within an image. This research aims to harness SAM’s capabilities for segmenting CXR images. Additionally, we explore the potential of another novel model called Grounding DINO. Grounding DINO is a zero-shot object detection model that utilizes a Swin (Shifted Windows) transformer for extracting image features and BERT (Bidirectional Encoder Representations from Transformers) for extracting textual information. It is primarily employed to detect objects in an image based on a provided text prompt, creating bounding boxes around the objects when certain text and box thresholds are met. These bounding boxes are then used as prompts for SAM to generate segmentation masks. The proposed framework has been assessed on CXRs obtained from patients at Emory Hospital in Atlanta, Georgia, USA and further evaluated using NIH clinical center’s CXR image dataset. © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

EHHGSO: Enhancing Hybrid Hugo Gas Solubility Optimization Algorithm Using Backtracking Technique 5

EHHGSO: Enhancing Hybrid Hugo Gas Solubility Optimization Al...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Mahanta, Ajoy Bin Nur, Md. Oshama Islam, Rakibul Azad, Saiful Wadith, Shadman Mahmud, Mufti Kaiser, M. Shamim Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Nottingham Trent University Department of Computer Science NottinghamNG118NS United Kingdom Nottingham Trent University Computing and Informatics Research Centre NottinghamNG118NS United Kingdom Nottingham Trent University Medical Technologies Innovation Facility NottinghamNG118NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9798350394290

Metaheuristic algorithms play a pivotal role in solving complex optimization problems. This study addresses a common challenge faced by such algorithms, i.e., getting trapped in local optima. This problem hampers the efficacy of metaheuristic algorithms in achieving globally optimal solutions. This limitation is also observed in Hybrid Henry Gas Solubility Optimization Algorithm (HHGSO)-a new metaheuristic based algorithm-which employs a cluster-based approach for population management. While this approach fosters exploration by diversifying the search process, it lacks adaptiveness in cluster assignments and the potentiality of hybridization with other algorithms. These shortcomings hinder the algorithm's ability to effectively escape local optima and harness the strengths of diverse optimization techniques. To mitigate these challenges, this study proposed an Enhanced HHGSO (EHHGSO) algorithm, an advanced hybridization of HHGSO. Here, a novel backtracking based technique is incorporated that can detect the trapping into local optima and can escape from it. Empirical findings substantiate the efficacy of EHHGSO in comparison to its ancestor, HHGSO for various benchmark functions. © 2023 IEEE.

关键词： Solubility

来源：评论

学校读者我要写书评

暂无评论

An Advanced Strategy for Efficient Agricultural Production using Machine Learning 2

An Advanced Strategy for Efficient Agricultural Production u...

引用

2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Athinarayanan, S. Selvi, R. Rajakumari, K. Arunadevi, R. Robinson Joel, M. Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Department of Information Technology Dr MGR Educational and Research Institute Chennai India Chennai India Department of Computer Science and Engineering Prince Shri Venkateshwara padmavathy Engineering College Tamil Nadu Chennai India Department of Information Technology Kings Engineering College Chennai India

ISBN: (纸本)9798350340235

Agriculture is very important to the prosperity of the country. Climate change has created a variety of problems for the agricultural academic system. Since machine learning (ML) provides effective and affordable solutions, it is the most effective method for solving problems. Crop yield prediction involves estimating the crop yield based on historical data and a variety of other factors, including the climate, sometimes the environment, the water source, and warmth. This study analyses and discusses the use of the linear regression technique for evaluating agricultural production based on data from the earlier study. This study uses geographic and meteorological data to anticipate winter wheat production. It is motivated by this data science issue. The project's goal is to find a solution to the cost loss issue. Actual agricultural data is used to build the models, which are then evaluated on samples. Using the vegetable yield forecasting model, end users (agriculturalist) may foresee crop production as they plant crops on agricultural land. The Linear Regression Algorithm method is used to forecast results with accuracy. The decision-making model's creation will be aided by the availability of a sizable dataset. © 2023 IEEE.

关键词： agriculturalist Decision-Making Linear Regression Machine Learning meteorological data

来源：评论

学校读者我要写书评

暂无评论

Imitation Game for Adversarial Disillusion With Chain-of-Thought Reasoning in Generative AI

引用

IEEE Access 2025年 13卷 95085-95093页

作者： Ching-Chun Chang Fan-Yun Chen Shih-Hong Gu Kai Gao Hanrui Wang Isao Echizen Information and Society Research Division National Institute of Informatics Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan School of Multidisciplinary Sciences Graduate Institute for Advanced Studies (SOKENDAI) Kanagawa Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviors. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalizes and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. Experimental results demonstrate that the proposed framework consistently neutralizes both deductive and inductive adversarial illusions across diverse white-box and black-box attack scenarios.

关键词： Games Predictive models Noise reduction Logic Cognition Adaptation models Semantics Object recognition Generative AI Data models

来源：评论

学校读者我要写书评

暂无评论

Handling Local Optima Trapped Situation by Improving the BAT Algorithm

引用

KSII Transactions on Internet and Information Systems 2025年第5期19卷 1564-1587页

作者： Younus, Waqar Abbas, Qamar Mazhar, Tehseen Iqbal, Muhammad Al-AlShaikh, Halah A. Shahzad, Tariq Saudagar, Abdul Khader Jilani Khan, Muhammad Amir Department of Computer Science International Islamic University Islamabad Pakistan Department of Computer Science & Information Technology Virtual University of Pakistan Lahore54000 Pakistan Institute of Computing and Information Technology Gomal University Dera Ismail Khan KP Pakistan Riyadh11432 Saudi Arabia Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia

This research proposes a chaos-enhanced bat algorithm to address global optimization problems. The Bat Algorithm (BA) is a popular swarm intelligence algorithms known for its effectiveness in solving optimization problems in both academic and real life scenarios. However, the classical approach is often susceptible to getting trapped in local optima. The current study presents an improved version of the Bat algorithm to overcome these limitations. We have enhanced the Bat Algorithm to boost its local search capabilities mitigate the issue of premature convergence. An improved search equation, incorporating additional valuable information, is introduced to generate better solutions. Additionally, a random walk Levy Flight mechanism is integrated into the Bat Algorithm to prevent it from being trapped in local optima. The proposed Bat Algorithm is applied to 10 benchmark functions within metaheuristic algorithms. While these algorithms have not been thoroughly investigated or manipulated in the past they prove useful in exploring the search space for optimal solutions. Experimental results demonstrate that the proposed algorithm outperforms established methods, including both original and enhanced approaches. Copyright © 2025 KSII.

关键词： Local search (optimization)

来源：评论

学校读者我要写书评

暂无评论

Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection

arXiv

引用

arXiv 2024年

作者： Guo, Yuejun Patsakis, Constantinos Hu, Qiang Tang, Qiang Casino, Fran Luxembourg Institute of Science and Technology Luxembourg Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece Information Management Systems Institute of Athena Research Centre Greece Department of Computer Science The University of Tokyo Japan Department of Computer Engineering and Mathematics Rovira i Virgili University Tarragona Spain

The significant increase in software production driven by automation and faster development lifecycles has resulted in a corresponding surge in software vulnerabilities. In parallel, the evolving landscape of software vulnerability detection, highlighting the shift from traditional methods to machine learning and large language models (LLMs), provides massive opportunities at the cost of resource-demanding computations. This paper thoroughly analyses LLMs’ capabilities in detecting vulnerabilities within source code by testing models beyond their usual applications to study their potential in cybersecurity tasks. We evaluate the performance of six open-source models that are specifically trained for vulnerability detection against six general-purpose LLMs, three of which were further fine-tuned on a dataset that we compiled. Our dataset, alongside five state-of-the-art benchmark datasets, were used to create a pipeline to leverage a binary classification task, namely classifying code into vulnerable and non-vulnerable. The findings highlight significant variations in classification accuracy across benchmarks, revealing the critical influence of fine-tuning in enhancing the detection capabilities of small LLMs over their larger counterparts, yet only in the specific scenarios in which they were trained. Further experiments and analysis also underscore the issues with current benchmark datasets, particularly around mislabeling and their impact on model training and performance, which raises concerns about the current state of practice. We also discuss the road ahead in the field suggesting strategies for improved model training and dataset curation. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipeline codes

来源：评论

学校读者我要写书评

暂无评论

Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

引用

23rd RoEduNet Conference: Networking in Education and research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

来源：评论

学校读者我要写书评

暂无评论

Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics

arXiv

引用

arXiv 2023年

作者： Reddy, Gurunath M. Zhang, Zhe Yu, Yi Harscoet, Florian Canales, Simon Tang, Suhua Digital Content and Media Sciences Research Division National Institute of Informatics SOKENDAI Japan Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Japan

We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Data Security and Protection: A Mechanism for Managing Data Theft and Cybercrime in Online Platforms of Educational Institutions

Data Security and Protection: A Mechanism for Managing Data ...

引用

2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Mohammed, Abubakar Kumar, Sanjeev Mu'Azu, Hammandikko Gaya Kumar, Rajiv Shah, Praveen Memoria, Minakshi Rawat, Amarjeet Gupta, Ashulekha Federal University Department of Information Technology Dutse Nigeria Himgiri Zee University Department of Computer Science & Information Technology Dehradun India Modibbo Adama University Department of Statistics and Operations Research Yola Nigeria Uttaranchal Institute of Management Uttaranchal University Department of Computer Applications Dehradun India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Management Studies Dehradun India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Advancement in technology has brought changes in educational institutions that leads to the adoption of several information system and online platforms. Students and staff were sharing important data through these platforms. This has exposed the institution to cybercrime activities by both internal and external agents of malicious users. Their motive is to gain access to the platforms in order to data related to fees and other academic records, or to distort the entire system to steal the data with the aim of committing an illicit transactions or activities. This paper aimed at providing security mechanism or model for data security and protection in educational institutions. It provides empirical studies related to data security and protection as well as cyberspace and online data theft in educational institutions. The paper recommends that educational institutions should adopt and implement the security mechanism and models to prevent cybercrime activities. It also recommends that sensitization should be organized for both students and staff on the use online platforms to prevent them from being exposed to malicious users. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Study of Image Data Security with Cloud

Study of Image Data Security with Cloud

引用

International Conference on Trends in Electronics and informatics (ICEI)

作者： Pronika Chawla Harshita Parihar Ankit Mathur Department of Computer Science & Engineering Faculty of Engineering & Technology Manav Rachna International Institute of Research and Studies Faridabad India

The on-demand availableness of computer systems resources, especially storage of data (cloud storage) and rectifying power, with no direct ongoing administration by the user is what they call cloud computing. Large cloud functions are usually spread among several locations, each of which constitutes a data center. Customers may save money on capital expenses by using cloud computing, which frequently takes a “pay-as-you-go” approach. Coherence in cloud computing is achieved by sharing resources. In today's world of the Internet, demand for cloud services is increasing drastically leading to the production of new services day by day. As the services increase, the data gets primarily targeted by spiteful users who attempt to steal the data for their own atrocious and unethical activities. Users and trustworthy applications are considering more security and privacy and services get more in demand. Moreover, this study has reviewed several algorithms such as CPE-ABE (Ciphertext policy attribute-based encryption), ABE, KP-ABE (Key policy attribute-based encryption) CSP (Constraint satisfaction problem), PKG, AES (Advanced Encryption Standards), SHA-1 (Secure Hash Algorithm), Photo encryption, Photo decryption, PRE, IDEA (International Data Encryption algorithm) and LSBG (Least Significant Bit Grouping) for image data security. As already discussed, the increasing threats and frauds around the world, safe and secure applications and services should be created to resolve this problem so that people can store data on a platform that can be relied on. This research study has discussed about the concept of what are the paradigms required for securing and protecting the data and securing the image data at an encrypted level. This study has reviewed several existing research works, studied different algorithms which have been used in different research articles, and compared their strengths and drawbacks accordingly. Different research works have been summarized in a form of ta

关键词： Cloud computing Privacy Data centers Image resolution Production Market research Encryption

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：