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检索条件"任意字段=5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023"

共 2608 条记录，以下是61-70 订阅

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A Survey on Adversarial Text Attacks on Deep learning Models in Natural Language Processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

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5th international conference on data science, machine learning and applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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machine learning Algorithms – the Effect of Training and Testing Process 5th

Machine Learning Algorithms – The Effect of Training and Te...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Vasundhara, S. Tejaswini, S. Sriyam, Addepalli Sai Department of Humanities & Mathematics G. Narayanamma Institute of Technology and Science Shaikpet Hyderabad500104 India G. Narayanamma Institute of Technology and Science Shaikpet Hyderabad500104 India

ISBN: (纸本)9789819780426

Newer perspectives toward technology aim to leverage current methods to achieve even greater heights. machine learning, a game-changer in the real world, assists machines in progressively improving their performance. It also enables software applications to accurately predict outcomes without explicit programming. Traditionally, machine learning algorithms use past data for predictions. In a typical machine learning environment, the primary focus is on data training. the collected data is divided into two purposes: one for training and the other for testing. Common practice suggests reserving a higher percentage of data for training the machine because the more the machine is trained, the better its results become. While the machine learns from the training data, its capabilities are assessed based on its performance on unknown data. If the machine can produce convincing results on unknown datasets, it is likely to perform well on real-time data. As a result, data testing takes centre stage. therefore, in this paper, the proposed idea is of using limited yet efficient datasets for training and reserving the rest of the data for testing the machine. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Comparative Analysis of Techniques Used for Software Fault Identification: A Survey 5th

A Comparative Analysis of Techniques Used for Software Fault...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Panda, Radhe Shyam Sharma, Manisha Tiwari, Rajesh Bhilai Institute of Technology Durg India CMR Engineering College Hyderabad India

ISBN: (纸本)9789819780426

In the domain of the software fault prediction numerous methods have been introduced and implemented using data mining techniques and machine learning models. Nevertheless, initial and early fault prediction is big challenging to be overcome and improvised with higher classification rate of fault prediction. In order to fix this issue several software solutions and approaches are suggested by many researchers. In this paper a comparative study is done amongst all these approaches. Some software solutions for defect prediction are based on the software metrics evaluation but they are time consuming. To resolve this considerable time complexity many researchers suggested the software defect prediction model incorporating the machine learning model and got the better result. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Implementation of Recent Advancements in Cyber Security Practices and Laws in India 5th

Implementation of Recent Advancements in Cyber Security Prac...

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5th international conference on data science, machine learning and applications

作者： Kumar, Bura Vijay Singh, Manvendra Arora, Vaishali Malik, Khushboo Nomani, M. Z. M. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Sharda Univ Sharda Sch Law Greater Noida India CHRIST Dept Law Delhi NCR Campus Ghaziabad India Aligarh Muslim Univ Fac Law Aligarh 202001 Uttar Pradesh India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. this essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. the community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry.

关键词： Cyber security law cyber crime data protection information technology

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AI-Driven Prediction of Cancer Recurrence 5th

AI-Driven Prediction of Cancer Recurrence

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Annapoorna, B. Reddy, M. Janga Satyanarayana, B. Ravi, M. Krishnaiah, Pokala Dileep, Chilukuri CMR Institute of Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

Cancer recurrence remains a critical concern in healthcare, impacting patient prognosis and survival. this review explores the role of artificial intelligence (AI) in predicting cancer recurrence, emphasizing its potential to enhance personalized care. Key AI techniques, including machine learning algorithms, deep learning, and multi-omics data integration, are discussed. the review also addresses the need for regulatory oversight and ethical considerations. AI-driven prediction of cancer recurrence has transformative potential in optimizing patient outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Predictive Modelling of Cardiovascular Health Using IoT data and machine learning 5th

Predictive Modelling of Cardiovascular Health Using IoT Data...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Krishnaiah, Pokala Dileep, Chilukuri Annapoorna, B. Reddy, M. Janga Satyanarayana, B. Ravi, M. CMR Institute of Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

the combination of Internet of things (IoT) data and machine learning has transformed the prognosis of cardiovascular health. Wearables, smart sensors, and continuous monitoring generate real-time, personalized data. machine learning deciphers intricate patterns, predicting cardiovascular events accurately. Traditional risk assessments rely on periodic checks, missing crucial changes. IoT, coupled with machine learning, enables continuous monitoring, detecting subtle variations. this proactive approach empowers early interventions, personalized treatments, and lifestyle recommendations. Predictive modelling using IoT and machine learning shifts healthcare from reactive to proactive, aligning with precision medicine goals. Despite promising outcomes, challenges like data security and interdisciplinary collaborations need addressing. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Wireless Sensor Network for Fault Detection Using Block Chain Technology Based Smart Grid Security 5th

Wireless Sensor Network for Fault Detection Using Block Chai...

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5th international conference on data science, machine learning and applications

作者： Sudhakar, A. V. V. Goswami, Chandrshekhar Neeraja, B. Jain, Amit Kumar Gupta, Sandeep Gowri, G. SR Univ Sch Engn Dept EEE Warangal Telangana India Sir Padampat Singhania Univ Fac Comp & Informat Udaipur 313601 Rajasthan India Govt Polytech Hyderabad Elect & Elect Engn Hyderabad Telangana India Poornima Univ Elect & Elect Engn Jaipur Rajasthan India Graph Era Univ Elect Engn Dept Dehra Dun Uttarakhand India Karpagam Inst Technol Dept Artificial Intelligence & Data Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the combination of blockchain technology and wireless sensor networks (WSNs) has shown promise in boosting the security and dependability of smart grid systems. In order to improve security, this research paper focuses on integrating blockchain technology into the use of WSNs for fault detection in smart grids. the design, advantages, and difficulties of this technique are covered in the paper along with a case study that illustrates how it might be used in real-world settings. the findings demonstrate that a WSN and blockchain technology combination offers a reliable and safe method for real-time detection of faults in smart grids. A module of IoT in a node routing practice of smart grid built on blockchain has improved the security of the smart grid network. the industrial study was conducted using a transfer convolutional network that uses Q-learning for defect identification in a network. Experimental evaluation has been done in terms of accuracy, RMSE, and bit error rate. A bit error rate of 68%, accuracy of 97%, and RMSE of 76% were achieved by the suggested technique. Given that security is the main obstacle to the introduction of smart grids, this proposed paradigm is useful for better security and fault detection over the network.

关键词： Wireless sensor network Fault detection Blockchain technology Smart grid security Q-learning Security

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machine learning-Based Fault Tolerance Techniques for VLSI Circuit Design 5th

Machine Learning-Based Fault Tolerance Techniques for VLSI C...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Reddy, MJanga Satyanarayana, B. Ravi, M. Krishnaiah, Pokala Dileep, Chilukuri Annapoorna, B. CMR Institute of Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

In the dynamic landscape of Very Large-Scale Integration (VLSI) circuit design, the relentless pursuit of enhanced performance and fault tolerance confronts challenges arising from shrinking transistor sizes and heightened component density. this paper explores the integration of machine learning (ML) techniques into VLSI fault tolerance, in a new era of adaptability and resilience. Various ML-driven fault tolerance strategies are investigated, encompassing anomaly detection, supervised learning for fault classification, fault localization and diagnosis, reconfiguration and self-repair mechanisms, predictive maintenance, adaptive testing, and reliability modelling. By leveraging data-driven insights, these techniques promise to revolutionize VLSI circuit design, mitigating the impact of faults and setting the stage for future advancements. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

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Design Space Exploration in VLSI Using machine learning for Power and Performance Optimization 5th

Design Space Exploration in VLSI Using Machine Learning for ...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Satyanarayana, B. Ravi, M. Krishnaiah, Pokala Dileep, Chilukuri Annapoorna, B. Reddy, M. Janga CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9789819780426

Design Space Exploration (DSE) in Very Large-Scale Integration (VLSI) is critical for optimizing power and performance in semiconductor design. Traditional manual approaches are impractical due to increasing circuit complexity. Integrating machine learning (ML) into DSE automates exploration, predicting power consumption based on configurations. ML-driven DSE reduces search time, leveraging reinforcement learning for iterative refinement. ML reveals non-intuitive design-power relationships, aiding discovery of energy-efficient architectures. this synergy empowers efficient trade-off navigation, advancing VLSI towards low-power, high-performance solutions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Space research

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Deep learning Algorithms for Tumours Classification in Medical Images 5th

Deep Learning Algorithms for Tumours Classification in Medic...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Dileep, Chilukuri Annapoorna, B. Janga Reddy, M. Satyanarayana, B. Ravi, M. Krishnaiah, Pokala CMR Institute of Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

this paper explores the application of deep learning algorithms in the accurate classification of tumours in medical images, addressing the critical need for precise cancer diagnosis. It reviews the utilization of various deep learning models, including Convolutional Neural Networks (CNNs), U-Net, Residual Networks, and others, in classifying tumours across different imaging modalities. the significance of deep learning in early detection, localization, and treatment planning is emphasized. the challenges and opportunities in this field are discussed, ranging from data availability and interpretability to data augmentation and interdisciplinary collaboration. By addressing these challenges, deep learning can significantly advance in cancer diagnostics, improving patient care. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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