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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Sangeetha, M. Karthikeyini, C. Vasundhara, S. Saravanan, D. Arunkumar, G. School of Computing SRMIST Department of Data Science and Business Systems Kattankulathur C India Excel Engineering College Department of ECE Tamil Nadu Komarapalayam India G.Narayanamma Institute of Technology &science Department of Humanities & Mathematics JNTUH Hyderabad India IFET College of Engineering Department of CSE Tamil Nadu Villupuram India Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh Madanapalle India

ISBN: (纸本)9781665456876

Skin Cancer is one of the most common cancer forms in many countries, it is considered to be one of the dangerous types in the sense that it is lethal and its occurrence over time has been dramatically high. It is one of the deadliest cancers among all diseases and has a large rate of mortality. The efficiency of the earlier approaches to assess one of the most hazardous melanoma diagnosis in dermoscopic criteria are not up to the mark. Therefore, in this research, the work has been carried out in three stages in order to detect melanoma in an efficient manner. In the first stage, prior to the implementation of the image segmentation technique, noise elimination and pre-processing steps are carried out to remove the noise and to achieve better execution results. This segmentation model focuses on the separation of the interesting portions from the background and collects the necessary information from the neighboring pixels of the same category. Gaussian analytical patterns are used to handle the heterogeneous regions/sections of dermoscopy images whose mean and variance can be dynamic. In addition, the third stage of work is based on the texture classification, which is proposed as the CSTC-Mel Identification Model. © 2022 IEEE.

关键词： Machine learning

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Evaluating Deep Learning Models for Customer Lifetime Value Forecasting Based on Hybrid DT and Naive Bayes Model

Evaluating Deep Learning Models for Customer Lifetime Value ...

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Communication & Computing (WCONF), World Conference on

作者： Nawab Akram Shobana M Harsh R Kathiriya Prasanthi Gottumukkala S. Rathika Rajasree R Magadh Professional Institute Patna Bihar India Department of Computer Science and Business Systems MKumarasamy College of Engineering Karur India Data Scientist at Big Analytixs Atlanta USA Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science (Artificial Intelligence & Data Science) Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Customer lifetime value, a core concept in direct response marketing for quite some time, is now receiving increasing attention from management at Boston University's School of Management. However, the majority of published works on the topic have mainly concentrated on lauding its usefulness as a decision-making metric, offering only one numerical example of how it is calculated or determined, incorporating it into broader discussions of profitability, and examining its function in decisions regarding customer acquisition and the tensions between acquiring and retaining customers. There are three steps to the suggested method: preprocessing, feature extraction, and model training. After archiving duplicate and missing data, data preparation is employed to eradicate missing values. The feature selection process incorporates K-means, principal component analysis (PCA), and customer lifetime value measurements. An increase in the model training procedure's accuracy was achieved by utilizing the Hybrid DT and naive bayes. The recommended approach achieves a higher accuracy rate of 96.03 % compared to its rivals, which includes naive bayes and Hybrid DT.

关键词： Training Accuracy Profitability Computational modeling Predictive models Feature extraction Bayes methods Numerical models Forecasting Principal component analysis

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Adaptive Learning in Multiagent systems Using Brain-Computer Interfaces

Adaptive Learning in Multiagent Systems Using Brain-Computer...

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Disruptive Technologies (ICDT), International Conference on

作者： Jagjit Singh Dhatterwal Kuldeep Singh Kaswan Nitin Thapliyal Kiran Malik Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Engineering Galgotias University Greater Noida UP India Department of Computer Science and Engineering Graphic Era Hills University Dehradun Uttarakhand India Department of Computer Science and Engineering Matu Ram Institute of Engineering and Management Rohtak Haryana India

Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate the integration of brain-computer interfaces (BCIs) as a novel means to facilitate adaptive learning in multiagent systems. BCIs establish direct communication channels between the human brain and external devices, enabling real-time monitoring and interpretation of neural activity. By leveraging BCIs, agents can capture valuable cognitive signals from human operators and use this information to adapt their decision-making processes and behaviors. We present a theoretical framework that outlines the incorporation of BCIs into multiagent systems and the mechanisms for adaptive learning. Additionally, we propose a comprehensive methodology for evaluating the effectiveness of this approach. The results of our experiments demonstrate the potential of adaptive learningin multiagent systems using BCIs, paving the way for new applications in various domains, including human-machine col-laboration, assistive technologies, and interactive gaming systems.

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Enhancing Residential Security with AI-Powered Intrusion Detection systems

Enhancing Residential Security with AI-Powered Intrusion Det...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Jeneetha Jebanazer J B.Vishnu Prabha B. Yasotha Jaisudha J C. Senthilkumar V.Samuthira Pandi Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Jeppiaar institute of Technology Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Tamil Nadu India

Using Intrusion Detection systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system's superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today.

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Deep Learning for Uneven data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network 5

Deep Learning for Uneven Data in Industrial IoT Using a Dist...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Gupta, Raj Kumar Naveena, N. Rao, B. Srinivasa Rs, Rajasree Sarkar, Swagata Kumar, Kallakunta Ravi Sardar Vallabhbhai Patel College Veer Kunwar Singh University Physics Department Bihar Ara Bhabua India Kongu Engineering College Computer Technology-UG Perundurai India Gokaraju Rangaraju Institute of Engineering & Technology Department of Computer Science and Engineering Hyderabad500090 India New Horizon College of Engineering Artificial Intelligence and Machine Learning Bangalore India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Sai Leo Nagar Chennai44 India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques. © 2023 IEEE.

关键词： Generative adversarial networks

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Long Short-Term Memory-Based Next Keyword Prediction

Long Short-Term Memory-Based Next Keyword Prediction

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Rajkumar. K Karthikeyan. S U. Hariharan Dept. of Data Science and Business Systems College of Engineering and Technology SRM Institute of Science & Technology Kattankulathur Tamilnadu India Department of CSE FET Jain (Deemed-to-be University) Bangalore Department of CSE Apex Institute of Technology Chandigarh University Punjab India

This paper focuses on a method that uses an LSTM model to predict the next word of a sentence. Based on Katz's Backoff model, it has been designed to improve and further succeed in our previous work. The main intention here is to show how effective the LSTM model is when compared to Katz's backoff model in terms of the number of n-grams required to predict the next word, and it also helps you predict more accurately. Here LSTM is used for processing, prediction and classification. Since the LSTM model is efficient and has been modified to the task at hand, the model offers very highly accurate predictions even though have used only a tiny portion of the entire corpus. When comparing the existing model, the proposed model predicts the word with accuracy of 90%.

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Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity

arXiv

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arXiv 2024年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

Professional networks — the social networks among people in a given line of work — can serve as a conduit for job prospects and other opportunities. Here we propose a model for the formation of such networks and the transfer of opportunities within them. In our theoretical model, individuals strategically connect with others to maximize the probability that they receive opportunities from them. We explore how professional networks balance connectivity, where connections facilitate opportunity transfers to those who did not get them from outside sources, and congestion, where some individuals receive too many opportunities from their connections and waste some of them. We show that strategic individuals are over-connected at equilibrium relative to a social optimum, leading to a price of anarchy for which we derive nearly tight asymptotic bounds. We also show that, at equilibrium, individuals form connections to those who provide similar benefit to them as they provide to others. Thus, our model provides a microfoundation in professional networking contexts for the fundamental sociological principle of homophily, that "similarity breeds connection" [29], which in our setting is realized as a form of status homophily based on alignment in individual benefit. We further explore how, even if individuals are a priori equally likely to receive opportunities from outside sources, equilibria can be unequal, and we provide nearly tight bounds on how unequal they can be. Finally, we explore the ability for online platforms to intervene to improve social welfare and show that natural heuristics may result in adverse effects at equilibrium. Our simple model allows for a surprisingly rich analysis of coordination problems in professional networks and suggests many directions for further exploration. © 2024, CC BY.

关键词： Professional aspects

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Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography

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Nuclear science and Techniques 2021年第4期32卷 70-83页

作者： Yin-Jin Ma Yong Ren Peng Feng Peng He Xiao-Dong Guo Biao Wei Key Laboratory of Optoelectronic Technology and Systems Ministry of EducationChongqing UniversityChongqing 400044China School of Data Science Tongren UniversityTongren 554300China School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA

The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to ***-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises quality and decreases diagnostic *** have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques,which have achieved impressive ***,most of these methods are directly performed on reconstructed LDCT images,in which some subtle structures and details are readily lost during the reconstruction procedure,and convolutional neural network(CNN)-based methods for raw LDCT projection data are rarely *** address this problem,we adopted an attention residual dense CNN,referred to as AttRDN,for LDCT sinogram ***,it was aided by the attention mechanism,in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT ***,the denoised sinogram was restored by subtracting the noise obtained from the input noisy ***,the CT image was reconstructed using filtered *** experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art ***,it can prevent the loss of detailed information and has the potential for clinical application.

关键词： Low-dose CT Sinogram denoising Deep learning Attention mechanism

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