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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Pandithurai, O. Dhiyaneswaran, P. Raj, S. B. Gokul Sriman, B. Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031689048

According to the World Health Organization (WHO) estimates that the millions of people worldwide suffer from visual impairment, whether partial or complete, which makes it challenging for them to detect obstacles and identify people around them. However, recent advancements in information technology and spatial cognition theory have created an opportunity for researchers and developers to explore innovative solutions that can empower visually impaired individuals, enhance inclusivity, and improve accessibility. To this end, a novel framework has been proposed that utilizes artificial vision, specifically an intelligent system based on a convolutional neural network (CNN) algorithm. This system automatically recognizes human and scene objects or obstacles in real-time, even in difficult situation with large moving object. The system provides the user with a complete information, including the position of located targets, and alerts the user via a voice message about obstaclesor known or unknown individuals. The proposed work aims to create a user-friendly technology that fulfills the essential needs of physically disabled individuals, providing them with an easy-to-use interface, convenience, portability, and cost-effectiveness. Ultimately, this approach enables blind users to navigate and manage indoor and outdoor locations that they may not have been aware of otherwise, enhancing their autonomy and quality of life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Anomaly Detection of Industrial Data Based on Multivariate Multi Scale Analysis 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Lu, Dan Li, Siao Zhao, Yingnan Han, Qilong College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

Anomaly detection stands as a crucial facet within the domain of data quality assurance. Notably, significant strides have been made within the realm of existing anomaly detection algorithms, encompassing notable techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and anomaly detection models founded upon Generative Adversarial Networks (GANs). However, a notable gap lies in the inadequate consideration of interdependencies and correlations inherent in multidimensional time-series data. This becomes particularly pronounced within the context of industrial evolution, where industrial data burgeons in complexity. To address this lacuna, a novel hybrid model has been introduced, synergizing the capabilities of GRU with structural learning methodologies and graph neural networks. The model capitalizes on graph structural learning to unearth dependencies linking data points across distinct spatial dimensions. Concurrently, GRU extracts temporal correlations embedded within data along a single dimension. Through the incorporation of graph attention networks, the model employs a dual-faceted correlation perspective for data prediction. Discrepancies between predicted values and ground truth are utilized to gauge errors. The amalgamation of predictive and scoring mechanisms enhances the model’s versatility. Empirical validation on two authentic sensor datasets unequivocally demonstrates the superior efficacy of this approach in anomaly detection compared to alternative methodologies. A notable augmentation is observed particularly in the recall rate, underscoring the method’s potency in identifying anomalies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Earthquake Analysis Using Long-Short Term Memory - LSTM

Earthquake Analysis Using Long-Short Term Memory - LSTM

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2024 International Conference on computer and Applications, ICCA 2024

作者： Salman, Ali El Mir, Gilbert Diab, Lea El Haber, Elias Haidar, Gaby Abou Achkar, Roger American University of Science and Technology Department of Computer & Communications Engineering Beirut Lebanon

ISBN: (纸本)9798350367560

The prediction and analysis of earthquakes have been a significant focus of scientific research for many years. Traditional methods have provided valuable insights, but their accuracy and dynamicity remain limited. The aim here is to address these challenges by employing deep learning techniques, specifically the Long Short-Term Memory (LSTM) algorithm, in conjunction with XGBoost as an ensemble learning technique. Earthquake analysis has been approached through various methodologies, with mixed results. Some methods have fallen short in delivering precise analysis, while others have grappled with the complexities of forecasting earthquakes. The introduction of deep learning in earthquake prediction has led to different outcomes compared to previous techniques. However, spatial distortion remains a persistent challenge. To tackle these challenges, this study utilizes the LSTM algorithm for global earthquake observation, combined with XGBoost. By employing influential and associated datasets, the model calculates a more accurate and straightforward time function. By combining the strengths of LSTM and XGBoost, this study aims to provide a more accurate and reliable earthquake analyzer model. The LSTM algorithm and XGBoost are addressing the challenges of spatial distortion and improving accuracy, dynamicity, and performance. © 2024 IEEE.

关键词： Long short-term memory

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Enhancing Bakery Industry Supply Chain Management Through Blockchain 5th

Enhancing Bakery Industry Supply Chain Management Through Bl...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Raj Pal Verma, Rajat Department of Computer Science and Engineering Pranveer Singh Institute of Technology UP Kanpur India

ISBN: (纸本)9789819771776

In Supply Chain, the Blockchain technology is important in the transformation of the Bakery Industry. The Blockchain technology is instrumental in ensuring product authenticity, transparency, and product traceability. This paper focuses on a Blockchain-Based technology that is envisioned to transform the Supply Chain Management (SCM) in the baking industry. In proving quality control and confidence in the food business, other factors considered in the design are interoperability and ongoing distribution of blockchain technology. By integrating blockchain technology and smart contract, the design creates a network that is safe and visible that follows up the bakery foods flow from raw items to the final consumer. The SCM system design anchors the Blockchain technology in each production stage, packaging, consignment, and distribution. Such an approach would ensure accountability and reduce the risk of fraud or fake products since the parties would have access to up-to-date, unchangeable information. In addition, application of Blockchain technology would increase the likelihood of traceability, aiding in the timely detection and resolution of any quality or safety issues. Thus, the partners would be better aware of the sources of ingredients and the processes of production and product handling, which would help win the clients’ trust and meet the regulations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bakeries

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AlertPilot: Video-Based Driver Fatigue Detection with Enhanced Face Tracking 5th

AlertPilot: Video-Based Driver Fatigue Detection with Enhanc...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Tiwari, Amit Kumar Maurya, Deepanshu Pandey, Kartikeya Tiwari, Sanjeev Yadav, Shubham Department of Computer Science and Engineering United Institute of Technology Uttar Pradesh Prayagraj India

ISBN: (纸本)9789819771776

The human face serves as a crucial communicator of various states and conditions. When a driver experiences fatigue, subtle changes in facial expressions, such as increased blinking and yawning, become apparent. This article introduces a novel system known as AlertPilot, designed to detect signs of driver fatigue using video analysis, thus eliminating the need for additional driver equipment. To overcome the limitations of existing algorithms, a new face detection algorithm is proposed to enhance tracking precision. Furthermore, a novel face detection approach utilizing 69 key facial landmarks has been developed. These landmarks are instrumental in assessing the driver’s condition. By analyzing facial cues, AlertPilot can issue fatigue alerts to the driver, primarily focusing on eye and mouth movements. Rigorous testing has demonstrated AlertPilot’s remarkable accuracy, achieving approximately 95% precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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A Dam Deformation Prediction Model Based on SVM-RFE 3rd

A Dam Deformation Prediction Model Based on SVM-RFE

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Cai, Shuo Zhang, Jie Gao, Huixin School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410076 China

ISBN: (纸本)9789819624676

Dams play a pivotal part in national economic development, and to protect the operation of dams, monitoring and prediction of dam deformation are crucial. In response to the fact that the commonly used HST (hydrostatic, season, time) model for predicting dam deformation in the past cannot explain the influence of environmental variables well, the measured temperature was used to replace the harmonic function in the HST model as the temperature component, and the lagged water levels were added as an influencing factor. Then, the feature set was filtered using the Recursive Feature Elimination (RFE) algorithm, which reduces the dimensionality of the original feature set to the same level as the HST model to alleviate the computational burden of the model and the Support Vector Machine (SVM) was used to build the model. Aiming at the sensitivity of SVM model prediction accuracy to parameters, the Particle Swarm Optimization algorithm was introduced to find the best combination of parameters for SVM, and the SVM-RFE dam deformation prediction model was established. The measured data of Shuibuya concrete-faced rockfill dam verified the validity of the model. The final analysis indicate that compared with the traditional model, the improved SVM-RFE dam deformation prediction model obviously improves the prediction performance, reflecting the superiority of the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harmonic functions

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots 1

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Negi, Akshit Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering & Information Systems Vellore India

ISBN: (纸本)9798331520540

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society. © 2025 IEEE.

关键词： Anthropomorphic robots

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Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture

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SN computer science 2023年第3期4卷 214页

作者： Dalvi, Pooja Pradeep Edla, Damodar Reddy Purushothama, B.R. Department of Computer Science and Engineering National Institute of Technology Goa India

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AlexNet Chest X-rays COVID-19 DenseNet-169 ResNet-50 VGG-16 VGG-19

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Smart Market Insights: A Deep Learning Approach to Trend Analysis 13

Smart Market Insights: A Deep Learning Approach to Trend Ana...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Ahmad, Shahnawaz Ansari, Mohd. Aquib Mair, Aakriti Hussain, Shahadat School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350380583

Stock prices are associated with the dynamic nature of the market;hence it is excruciating to predict the numbers. It has always been tough to invest in a set of assets, the oddity of the financial market does not allow traditional models to predict tomorrow's asset values with higher accuracy. Moreover, traditional models lack in finding interdependency among different sectors, overfitting of historical data, and other nonlinear factors. In this review, we examine machine learning (ML) techniques that are applied to stock market prediction. Making computers accomplish activities that ordinarily need human intellect is known as ML, and it is now the most popular trend in scientific study. According to our results, we have concluded that our Long-Short Term Memory model (LSTM) model has predicted prices with higher accuracy as compared to simpler models. The focus of this article is to develop a model for predicting future stock market values using recurrent neural networks (RNN), specifically the LSTM. Moreover, it would also highlight the key features of deep learning and why ML models are preferred over linear models for forecasting in the markets. © 2024 IEEE.

关键词： Prediction models

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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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