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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Mahaveerakannan, R. Rajakumar, B. Sharma, Rajesh R. Pushpa, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of AI & DS JNN Institute of Engineering Tamil Nadu Chennai601102 India Department of Computer Science and Engineering Alliance College of Engineering and Deaign Alliance University Bangalore India Department of Computer Science and Engineering St. Peter's Institute of Higher Education and Research Avadi Chennai54 India

ISBN: (纸本)9798350374933

According to recent data released by the WHO, lung tumours are the top cause of death worldwide. One of the promising areas for effective lung cancer detection is tumour segmentation. To determine the location of tumours and the extent of malignancy, doctors use computerised tomography (CT) scans. Radiation oncologists face a daunting and time-consuming challenge with medical imaging segmentation that requires handcrafting. This article proposes a comprehensive approach for early detection of lung cancer using CT scan imaging as a solution to this challenge. In order to improve patients' chances of survival, this study presents an automated diagnostic algorithm. Part one of the suggested lung cancer diagnostic system is a segmentation constituent built on the UNEt TRansformers (UNETR) network, and part two is a classification component that uses the output segmentation component to label the output as benign or malignant, built on the self-supervised network. Pufferfish Optimisation Algorithm (POA) identifies the best values for UNETR's trainable parameters. The suggested practise offers a potent resource for the early detection and treatment of lung cancer by utilising data from 3D-input CT scans. The three-stage suggested lung cancer classification model makes use of a modified U-Net for detection. To improve segmentation and classification outcomes, a large amount of trials have been conducted. © 2024 IEEE.

关键词： Lung cancer

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Design and Development of Cloud and IoT-Based Monitoring System for Environmental Health and Safety in Homes

Design and Development of Cloud and IoT-Based Monitoring Sys...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Baby Rani, N. Krishna Prakash, R. Adusumilli, Manasa Sunil Kumar, V. Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women Dhulapally It Department Hyderabad India Sona College of Technology Department of Information Technology Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Yelahanka Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Residential pollution is portrayed as a key contributor to health problems and environmental damage since it comes from indoor and outdoor sources. To discover a solution, a monitoring system should be built and capable of measuring the level of pollutants in typical residential environments. The suggested method makes use of cloud computing and the Internet of Things to measure pollution in residential areas. The goal is to monitor indoor air quality, gather data, and then communicate those findings with users in real time through cloud storage. An STM32 CPU, an ESP8266 wireless LAN module, a multisensory and hazardous substances are used in suggested system. According to the findings, the error rates for PM 2.5 and noise are 6-7% and 1%, respectively, while the error rate for readings of hazardous gases is 6%. The sensors are updated in 0.76 seconds, and an alarm results in a response of 2 seconds. © 2023 IEEE.

关键词： Cloud Computing Data analysis Internet of Things Monitoring system Visualization Wireless communication technologies

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Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

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Intelligent Automation & Soft Computing 2023年第8期37卷 1925-1940页

作者： Dong-Hoon Shin Seo-El Lee Byeong-Uk Jeon Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Department of Public Safety Bigdata Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-siGyeonggi-do16227Korea

Recently,the importance of data analysis has increased significantly due to the rapid data *** particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing *** missing values in data lead to the decreased predictive performance of *** missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image ***,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication *** study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these *** proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial *** results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive *** this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.

关键词： Missing value adversarial autoencoder spatiotemporal feature extraction

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Securing Data Using Visual Cryptography 4

Securing Data Using Visual Cryptography

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Dhand, Geetika Pahwa, Nishit Bhadri, Roopin Rastogi, Shikhar Niranjanamurthy, M. Maharaja Surajmal Institute of Technology Computer Science and Engineering Delhi India BMS Institute of Technology and Management Department of AI and ML Avalahalli Karnataka Bangalore560064 India

ISBN: (纸本)9781665456357

Data security is paramount in this modern era. Many corporations have established selective or their own cryptography schemes for information gathering and communication. The need of securing sensitive information is ever-so-increasing due to the rise of cyber thefts and online scams. Visual cryptography is a popular cryptographic technique that consists of hiding visual information like audio, video, image, text etc., in such a manner that nobody but the authorized groups who have complete access to the encrypted data, can collectively or individually reveal the original information. In this paper, we elaborate on two proposed visual cryptographic techniques for image encryption: one uses Caesar Cipher and RSA with a modified (k, N) share generation algorithm, and the other uses chaotic logistic mapping and DNA encoding. To analyze the security and robustness of these proposed algorithms, we will be performing several security tests such as time complexity, histograms, number of pixel change ratio, peak signal-to-noise ratios, information entropy, mean square error, and many other tests. © 2022 IEEE.

关键词： Cryptography

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Energy-Efficient Cluster Based Routing Protocol to Enhance the Lifetime of Wearable Wireless Body Area Networks (WBAN) 21

Energy-Efficient Cluster Based Routing Protocol to Enhance t...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Mohanty, Rasmita Kumari Kanth Motupalli, Ravi Manju, D. Gangappa, Malige Gouthami, B. Mounika, G. and AI&DS Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350358230

The paper proposes a protocol for Wireless Body Area Networks (WBANs) that aims to prolong the network's lifetime by reducing overall energy consumption. WBANs are a developing field in computer technology that enables medical practitioners to remotely observe patients and provide accurate treatment. Routing is a critical aspect of WBANs as it establishes paths between sensor nodes and a coordinator node. The proposed protocol employs a multi-hop routing technique to transfer body sensing data from various sensors deployed on the human body to the sink node. The main objective is to optimize the average network lifetime by minimizing energy consumption. To achieve this, the protocol employs a Mamdani Fuzzy Logic Controller (MFLC) approach choose a parent node based on residual energy in a multi-hop-based network. This is effective in prolonging the network lifetime of WBANs. Overly, the paper introduces a protocol for WBANs that focuses on energy-efficient multi-hop routing using fuzzy logic to select parent nodes. The proposed approach aims to prolong the network lifetime of WBANs and is evaluated through NS3 simulations. © 2023 IEEE.

关键词： Energy efficiency

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ai-Driven Cybersecurity Strategies for ISPs: Balancing Threat Mitigation and Monetization

AI-Driven Cybersecurity Strategies for ISPs: Balancing Threa...

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20th International Conference on Cyber Warfare and Security, ICCWS 2025

作者： Singh, Nakul Kumar, Shreyas AI and Cybersecurity Practitioner San Francisco United States Department of Computer Science and Engineering Texas A&M University United States

ISBN: (纸本)9781917204316

As traditional revenue streams from bundled internet, voice, and TV services decline due to the rise of OTT platforms like Netflix and voice-over-IP services such as WhatsApp, Internet Service Providers (ISPs) are seeking innovative strategies to remain competitive. This white paper highlights the pivotal role of ai-driven cybersecurity in securing household video and data traffic across broadband, video streaming, and smart home devices, offering ISPs a powerful tool to enhance customer experiences and boost Average Revenue Per User (ARPU). By leveraging ai technologies, ISPs can safeguard and optimize the flow of data across connected homes, ensuring a seamless, secure, and high-quality experience for users. ai-driven cybersecurity solutions enable ISPs to monitor and protect video streaming content, gaming applications, and smart home devices such as IP cameras. These solutions not only enhance security but also improve network performance by identifying and mitigating potential vulnerabilities in real time. By integrating ai-powered cybersecurity tools with real-time Quality of Experience (QoE) analytics, ISPs gain deep insights into user behavior and device interactions. This allows them to proactively address issues such as data breaches, service interruptions, and latency, ensuring uninterrupted and high-quality service delivery. Armed with these insights, ISPs can create personalized service bundles that combine premium cybersecurity features with high-demand offerings such as 4K streaming, low-latency gaming, and smart home automation. For example, ISPs can offer tailored packages that include advanced firewall protection, parental controls, and IoT device management alongside premium content and high-speed internet options. These ai-driven bundling strategies not only enhance the security and performance of connected devices but also unlock new revenue streams by upselling premium packages to customers. Additionally, value-added features such as real-time

关键词： Quality of service

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Prospector Heads: Generalized Feature Attribution for Large Models & Data 41

Prospector Heads: Generalized Feature Attribution for Large ...

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41st International Conference on Machine Learning, ICml 2024

作者： Machiraju, Gautam Derry, Alexander Desai, Arjun Guha, Neel Karimi, Amir-Hossein Zou, James Altman, Russ B. Ré, Christopher Mallick, Parag Department of Biomedical Data Science Stanford University United States Cartesia AI United States Department of Computer Science Stanford University United States Department of Electrical & Computer Engineering University of Waterloo Canada Department of Radiology Stanford University United States

Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for ml models in scientific and biomedical domains. Current methods for feature attribution, which rely on "explaining" the predictions of end-to-end classifiers, suffer from imprecise feature localization and are inadequate for use with small sample sizes and high-dimensional datasets due to computational challenges. We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods that can be applied to any encoder and any data modality. Prospector heads generalize across modalities through experiments on sequences (text), images (pathology), and graphs (protein structures), outperforming baseline attribution methods by up to 26.3 points in mean localization AUPRC. We also demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data. Through their high performance, flexibility, and generalizability, prospectors provide a framework for improving trust and transparency for ml models in complex domains. Copyright 2024 by the author(s)

关键词： Data assimilation

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Composing Like an Ancient Chinese Poet:Learn to Generate Rhythmic Chinese Poetry

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Journal of computer science & Technology 2023年第6期38卷 1272-1287页

作者：何明陈艳赵洪科刘淇吴乐崔羽曾贵华刘贵全 Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China AI Laboratory Lenovo ResearchBeijing 100193China Baidu Incorporated Beijing 100193China College of Management and Economics Tianjin UniversityTianjin 300072China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Risk Control Department OPayBeijing 100010China

Automatic generation of Chinese classical poetry is still a challenging problem in artificial ***-cently,Encoder-Decoder models have provided a few viable methods for poetry ***,by reviewing the pri-or methods,two major issues still need to be settled:1)most of them are one-stage generation methods without further polishing;2)they rarely take into consideration the restrictions of poetry,such as tone and ***,some an-cient Chinese poets tended first to write a coarse poem underlying aesthetics and then deliberated its semantics;while oth-ers first create a semantic poem and then refine its *** this basis,in order to better imitate the human creation procedure of poems,we propose a two-stage method(i.e.,restricted polishing generation method)of which each stage fo-cuses on the different aspects of poems(i.e.,semantics and aesthetics),which can produce a higher quality of generated *** this way,the two-stage method develops into two symmetrical generation methods,the aesthetics-to-semantics method and the semantics-to-aesthetics *** particular,we design a sampling method and a gate to formulate the tone and rhyme restrictions,which can further improve the rhythm of the generated *** results demon-strate the superiority of our proposed two-stage method in both automatic evaluation metrics and human evaluation met-rics compared with baselines,especially in yielding consistent improvements in tone and rhyme.

关键词： aesthetics poetry generation polishing semantics two-stage method

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Quantum Cryptography Techniques for Enhancing Security in IoT Communications

Quantum Cryptography Techniques for Enhancing Security in Io...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naga, Suresh Bysani Venkata Kotiyal, Arnav Battula, R.S.S. Raju Patil, Harshal Dharangutti, Yogesh Natrayan, L. Eightfold.ai Ex - Cohesity CA United States Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Aditya University Department of Computer Applications Surampalem India Iilm University School of Computer Science & Engineering Greater Noida India Department of Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha Kuthambakkam India

ISBN: (纸本)9798350375442

A system of wirelessly communicative sensors embedded in a person's clothing. In order to remotely monitor and treat a patient's health, sensor devices are embedded in clothing, skin, and the human body itself. Healthcare, the military, sports, and remote treatment are just a few of the modern WBSN applications. It improves people's quality of life by allowing for more precise and quicker disease diagnosis as well as more timely care and more effective treatment. Even if a patient is far away from medical care, WBSN can still save their life. Despite the numerous benefits that WBSN technology brings to healthcare systems, there are legitimate concerns about security and privacy when it comes to collecting and transmitting physiological data about humans in an open setting. Ensuring the security of physiological data during transmission in a wireless environment is a challenging but crucial task. Key distribution, authentication, data integrity, and data confidentiality are just a few of the many security issues plaguing the WBSN. In tackling the WBSN security issues, this research contributes significantly. Secure transmission in wireless networks and high attack resistance are both provided by the cryptographic computational process. Nevertheless, the unsecure transmission of keys across networks is the sole vulnerability of public and private key cryptography methods. Attackers can easily crack traditional key sharing protocols used by communicating parties. Intruders can compromise the system's security in its entirety if they obtain the secret key. In WBSN, a secure way to generate and distribute secret keys is required. The EBB84QCP is a method for creating and sharing secret keys between communicating parties. Rather than using two prime numbers, the Enhanced and Modified RSA Cryptosystem (EMRSACS) suggests using four prime numbers for key generation, encryption, and decryption. Data transmission in wireless networks still involves a secret quantum key that is

关键词： Quantum cryptography

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A Robust Data-Driven Predictive Maintenance Framework for Industrial Machinery using Explainable Machine Learning Techniques 9

A Robust Data-Driven Predictive Maintenance Framework for In...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Simi, T. Resmi, N.G. Muthoot Institute of Technology and Science Department of Ai and Ds Kerala Ernakulam India Muthoot Institute of Technology and Science Department of Computer Science and Engineering Kerala Ernakulam India

ISBN: (纸本)9798350314090

The best performance and longevity of industrial machinery are dependent on predictive maintenance. The proposed framework is a reliable and effective predictive maintenance tool that makes use of the widely used NASA-bearing dataset. It uses advanced machine learning algorithms in a multi-step procedure that includes data preparation, anomaly identification, and fault categorization. Then we tried to learn the underlying patterns in the data using an autoencoder, a potent unsupervised learning technique, and then set a threshold to detect anomalies. Anomalies are defined as deviations from the reconstructed signal, which the autoencoder is trained to recreate from the input data. Next, we employed k-shape clustering, a state-of-the-art time-series clustering method, to group similar patterns together in the latent representation obtained from the autoencoder. This enables us to obtain more accurate anomaly labels and improve fault classification accuracy. To further enhance the accuracy of the framework, we extracted several features from the data, including the poisson process, latent representation, frequency, and time domain features. These features are then used as inputs to a decision tree classifier, a popular machine-learning algorithm that can handle both categorical and numerical data. Additionally, we experimented with the gradient boost classifier, a boosting algorithm that improves the performance of the decision tree classifier. The experimental results demonstrate that our proposed framework achieves an accuracy of 95 percent using the gradient boost classifier, outperforming traditional approaches. The combination of the autoencoder-based anomaly detection and the decision tree classifier with the extracted features provides a powerful predictive maintenance framework that can improve the reliability and performance of machinery, reduce downtime and maintenance costs, and ultimately enhance overall productivity in various industrial settings. © 2023

关键词： NASA

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