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International Conference on IoT, Intelligent Computing and Security, IICS 2021

作者： Janakiraman, Sengathir Deva Priya, M. Christy Jeba Malar, A. Padmavathi, S. Raghunathan, T. Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Sri Krishna College of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789811981357

Cervical cancer is the most common type of cancer affecting the Indian women population. It is determined to be prevented by periodic screening which identifies and treats precancerous lesions in contrast to other cancers. Diversified screening methodologies that include traditional and advanced technologies are available for screening women cervix for the purpose of detecting cervical cancers and pre-cancers. No single screening test is perfect due to the manual error inherent in each cervical cancer detection technique. computer-assisted approaches that use graph cut-based segmentation is potential in detecting abnormalities from the pap smear cell. In this paper, Iterated Shape-Bias Graph Cut-based Segmentation (ISBGCS) method is proposed for achieving excellence in diagnosing cervical cancer, since they are capable of accurately deriving the boundaries of nuclei and cytoplasm even when the boundaries are hazy and overlapping. This ISBGCS method facilitates effective detection of nuclei and cytoplasm boundaries even under irregular staining of pap smear cell screening process. In specific, it considers local edge consistencies and global shape with shape priors simultaneously to evaluate the segmentation process. The resultant cues are formulated into a graph cut with local and global shape priors for achieving optimal segmentation during cervical cancer diagnosis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cells

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FlashHack: Reflections on the Usage of a Micro Hackathon as an Assessment Tool in a Machine Learning Course 2024

FlashHack: Reflections on the Usage of a Micro Hackathon as ...

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1st ACM Virtual Global Computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Indra, R. Parthasarathy, P.D. Ambasana, Jatin Satavlekar, Spruha Department of Information Science and Engineering B.M.S. College of Engineering Bengaluru India Department of Computer Science and Information Systems Bits Pilani KK Birla Goa Campus Goa India Centre for Educational Technology Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798400705984

Machine learning (ML) course for undergraduates face challenges in assessing student learning and providing practical exposure. Group project-based learning, an increasingly popular form of experiential learning in CS education, encounters certain limitation in participation and non-participation from a few students. Studies also suggest that students find longer programming assignments and project-based assessments distracting and struggle to maintain focus when they coincide with other courses. To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon that combines project-based learning with Hackathon elements. Engaging 229 third year CS undergraduate students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe. Assessment criteria emphasized machine learning application, problem-solving, collaboration, and creativity. Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors. This experience report outlines the Hackathon design and implementation, highlights successes and areas for improvement making it feasible for replication by interested computing educators. © 2024 ACM.

关键词： Adversarial machine learning

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Intelligent Irrigation Management: An IoT-Driven Approach with Real-Time Data Integration

Intelligent Irrigation Management: An IoT-Driven Approach wi...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kanaga Priya, P. Prasanna Blessy, M. Suguna, R.K. Narmada, C. Devi, G. Ramesh Kalyan, G. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India RVS Technical Campus Department of Computer Science and Engineering Coimbatore India SNS College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350319200

In the ever-evolving landscape of agriculture, the need for precision and efficiency has never been more critical. This paper introduces a cutting-edge irrigation management system that leverages the power of the Internet of Things (IoT) and real-time data integration. Instead of traditional irrigation methods, this system adapts irrigation practices dynamically based on soil moisture and plant growth stages. Through a seamless synergy of wireless IoT sensors and web applications, this solution provides farmers with a robust platform to remotely monitor and control irrigation processes. Soil moisture data is collected and analyzed in real-time, ensuring that water is delivered precisely when and where it's needed. Beyond soil data, this system integrates external sources of information, including real-time weather data encompassing rainfall probability, temperature, and wind speed. This data fusion empowers farmers to make informed decisions and fine-tune their irrigation strategies from anywhere, at any time. The project's scalability is a hallmark, capable of serving the needs of small-scale farms as well as extensive agricultural operations, and adaptable to a wide variety of crops. By embracing technology, this IoT-driven irrigation management system represents a significant stride towards sustainable and efficient agriculture, promising not only resource conservation but also increased crop health and resilience. © 2023 IEEE.

关键词： IoT Irrigation Precision Agriculture Real-Time Data Integration Soil Moisture Monitoring Sustainable Farming Wireless Sensors

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Person identification based on voice biometric using deep neural network

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International Journal of Information technology (Singapore) 2023年第2期15卷 789-795页

作者： AL-Shakarchy, Noor D. Obayes, Hadab Khalid Abdullah, Zahraa Najm Department of Computer Science Faculty of Computer Science and Information Technology Kerbala University Kerbala Iraq College of Education for Humanities Studies University of Babylon Babylon Iraq

Nowadays in all everyday transactions, technological progress has become an intrinsic characteristic that depends on such electronic applications as financial and banking transfers, health care, project management, and other crucial life aspects. The core of these applications is person Identification and/or verification steps which can be considered one of the complicated limitations. Accordingly, the employment of biometric attributes can yield promising outcomes in these fields. A One’s voice is a unique bio-feature whereby people can be authenticated and precludes others from assuming a one’s identity without their previous knowing or assent. This work proposes a model with a new architecture to identify the person by exploiting the unique individual characteristics available in one’s voice based on deep learning. An augmentation method is utilized to increase the samples in the available dataset. The available temporal information at an input audio file is analysed then feature maps from this information are extracted which represent the salient temporal feature (time-domain features). The decision is made based on tracking these voice features over time. Successful and promising results are achieved through this work, the accuracy is close to 99.81% (± 1.78%) and the values of loss function are close to 0.009 over VoxCeleb1 dataset for identifying 40 subjects. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Batch normalization layer (BN) One Dimensional Convolutional Neural Networks (1DCNN) Person identification PI Pooling layer (Pool) Rectified linear units (ReLU) Voice biometric

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Decoding Working Memory Representation Based on single-trial EEG By a Deep Learning Framework 1

Decoding Working Memory Representation Based on single-trial...

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1st International Workshop on Brain-computer Interfaces (BCI) for Multimedia Understanding, BCIMM 2024, co-located with ACM Multimedia 2024

作者： Li, Cheng Chen, Chunli Tian, Yin Xu, Peng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Life Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China School of Life Health Information Science and Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400711893

Although current research has made significant progress in decoding working memory (WM) representation information, this area still faces several limitations that cannot be ignored. These limitations mainly include a lack of understanding of the dynamic neural mechanisms of WM and the limited feature extraction capability that traditional machine learning encounters when dealing with nonlinear temporal data. Inspired by the deep learning model - attention-based temporal convolutional networks (ATCNet), the decoding of representation information for WM has seen a new light. The ATCNet, through its unique attention mechanism and temporal convolutional networks (TCN) structure, demonstrates potential advantages in processing nonlinear temporal data and capturing dynamic patterns. In this deep learning framework, we successfully decoded the orientation information during a WM task based on single-trial electroencephalogram (EEG) with the average accuracy of 85.81%. The findings demonstrate the potential applications of ATCNet in decoding WM and suggest that EEG signals carrying task-related information can be effectively utilized for robustly decoding memory representations. © 2024 Copyright held by the owner/author(s).

关键词： Spatio-temporal data

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Empirical Method for Thyroid Disease Prediction Using a Deep Learning Techniques 8

Empirical Method for Thyroid Disease Prediction Using a Deep...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Reddy, Reddyvari Venkateswara Monisha, G.S. Ali, Ahmed Mudassar Yamini Devi, Palivala Madhusudhan, M.V. Sucharitha, Yadala CMR College of Engineering & Technology CSE-Cyber Security Hyderabad India Panimalar Engineering College Computer Science and Engineering Chennai Poonamalle India S. A. Engineering College Inforormation Technology Chennai India Vignana Bharathi Institute of Technology Computer Science and Engineering Hyderabad India Presidency University Computer Science and Engineering Bengaluru India VNR Vignana Jyothi Institute of Engineering and Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350334012

Thyroid issues are quite general, effecting millions of people across the globe. The thyroid gland is susceptible to a wide range of illnesses, including hypothyroidism, hyperthyroidism, and malignancy. The symptoms of hypothyroidism are often very unpleasant. Timely identification of thyroid disorders is greatly aided by accurate categorization and machine learning. Treatment times for patients will be impacted by this rapid categorization. If we want to save time and have fewer mistakes made by radiologists, automatic and exact thyroid nodule recognition in ultrasound images is essential. The use of medical imaging has increased dramatically as a trustworthy and significant data source for the growth of machine learning (ML) strategies. In this study, we apply numerous diverse ML strategies to the dataset and accomplish a comparative examination of their efficiency so that we can more perfectly anticipate the disease utilizing the factors recognized by the dataset. In addition, the dataset has been tweaked to improve classification prediction accuracy. Both the sampled and non-sampled datasets were classified so that the two could be contrasted more precisely. With some data tweaking, we were able to boost the random forest algorithm's accuracy to 99 percent, with 97 percent specificity. © 2023 IEEE.

关键词： Forecasting

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Personalized Assistance Through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th International Conference on Information and Communication technology for Intelligent systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the Human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely HUMANVOICE. The most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. The Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. The system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Engineering education

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Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization technique 21

Accurate Classification of Cervical Cancer Based on Multi-la...

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21st International Learning and technology Conference, L and T 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. Youssef, Rasha Y. The Higher Institute for Management and Information Technology Minya Egypt Nahda University Faculty of Computer Science Computer Science Department Beni Suef Egypt Effat University College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Cancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods. © 2024 IEEE.

关键词： Diseases

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AI-Inspired Algorithm for the Automated Recognition of the World’s Oldest Script “Brahmi”

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SN computer science 2025年第1期6卷 1-12页

作者： Jain, Trang Jain, Arpit Dwivedi, Rakesh Kumar Saxena, Rakhi College of Computing Sciences and Information Technology Teerthanker Mahaveer University UP Moradabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada India Department of Computer Science Deshbandhu College University of Delhi New Delhi India

One of the internationally known oldest script is Brahmi whose digitisation may be helpful for the archaeologists as well as it may help in the digitisation of other languages. Optical character recognition techniques with genetic algorithms will be quite useful for this purpose. In this paper, a review of the work, done on the images of Brahmi letters using CNN till now, is presented. It will be useful to check the possibility of improvement in the performance of a model used for character recognition. A model is designed where tuning of hyper parameters is done using genetic algorithms which improves the accuracy of recognition to 97%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ashok shilalekh Brahmi script CNN Kharavela shilalekh OCR

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FedWM: Federated Crowdsourcing Workforce Management Service for Productive Laziness

FedWM: Federated Crowdsourcing Workforce Management Service ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Shi, Zhuan Yao, Zhenyu Yi, Liping Yu, Han Zhang, Lan Li, Xiang-Yang University of Science and Technology of China School of Computer Science and Technology China Univerisity of Liverpool Department of Mathematical Sciences United Kingdom Nankai University College of Computer Science China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350304855

Federated crowdsourcing, as a dynamic privacy-preserving distributed machine learning approach, has attracted significant research attention recently. Compared to federated learning (FL), clients can dynamically collect and label fresh data as required, and train model on the updated data. Existing research has mainly focused on incentivizing clients to spend more effort on data collection and labelling in order to improve FL model performance. However, as data collection and labeling require human effort, they need to balance work and rest. This need has been overlooked by existing federated crowdsourcing research. In this paper, we propose the Federated Workforce Management (FedWM) approach to bridge this important gap. It first measures the contribution of each client to the FL model, and estimates the urgency collecting new labelled data based on the rate of change of the contribution. Then, FedWM computes the working time taking into account of the client's maximum productivity and self-reported mood. Finally, it takes both the urgency level of obtaining new data and clients' productivity into consideration to provide scheduling services that advise the clients on work-rest balance in a given time slot based on Lyapunov optimization. Through theoretical analysis, we provide the performance bounds of FedWM. Through extensive experiments based on real-world datasets, we demonstrate that FedWM achieves significantly more advantageous tradeoffs between client rest and FL model performance compared to existing approaches. To the best of our knowledge, it is the first federated crowdsourcing framework designed to achieve productive laziness. © 2023 IEEE.

关键词： Crowdsourcing

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