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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Ramya, I.M. Jagadeesh, N.B. LeenaShruthi, M.H. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

There has been a lot of focus on agricultural image dispensation research in the previous years. computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision. © 2023 IEEE.

关键词： K-means clustering

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Prediction of Rainfall in Karnataka Region using optimised MVC-LSTM Model

Prediction of Rainfall in Karnataka Region using optimised M...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Achyutha Prasad, N. Srinivas, Chetana Jagadeesh, N.B. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

Forecasting precipitation is a prominent topic of study in meteorology. Predictions of precipitation using statistical analysis, learning methods are only a few of the methods that have been offered in the past. Organizations tasked with preventing natural catastrophes might benefit from using meteorological time series data prediction in their decision-making processes. The volume, dimension, and frequency of updates to Time Series data are quite high. The study of period sequence data for forecasting is a crucial part of the practical application. Time series data analysis allows for more precise rainfall forecasts, which is useful for assessing the severity of potential droughts and floods. Precipitation forecasting publications have employed a wide range of approaches. This improved forecast accuracy may be attributed to these methods. In this research, we apply a deep learning technique to examine the precipitation records from the Karnataka Division. This article presents a network consisting of a generator and a predictor for predicting spatial-temporal rainfall data. To imprisonment spatial correlations and build high-resolution data from sparse comments, it uses a multi-layer perceptron (MLP) in its generative module. A Multivariate Convolutional (MVC-LSTM) network is used to create the prediction unit;this network is able to capture the interplay between many variables and their temporal and spatial correlations. The Honey Badger approach finds the appropriate weight for the LSTM, which improves the model's ability to classify data. Additionally, this report suggests several avenues for further study in the fields of rainfall prediction and time series data analysis. © 2023 IEEE.

关键词： Decision making

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Video Chat using WebRTC with In-built Sign Language Detection 3

Video Chat using WebRTC with In-built Sign Language Detectio...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Sendhan, A. Saran, J.U. Vidhya Sagar, P.V. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350306989

Many research efforts into video conferencing technology have focused on audio-only interactions. Text chat, on the other hand, has many uses and is often used in conjunction with the audio channel in multimedia conferencing systems because of the many benefits it offers to users in a variety of contexts. Video chat on the other hand has been providing participants to participate not only through voice but also through visual clues, creating a richer and more immersive communication experience. With the development of technology, video chat has transformed how people cooperate and communicate across distances by becoming more accessible and user-friendly. This study details a prototype program that uses video chat with in-built sign language identification to help the hearing-impaired and the speech-impaired. The prototype is constructed from WebRTC to provide realtime communication for the application, on top of which the object detection model from Tensor Flow is integrated to provide hand sign detection and recognition for the user. We focus our testing on the bandwidth and sign language detection needs of customers in order to provide improved support for video chat. We then talk about how this evaluation's findings can inform the evolution of video chatting software. © 2023 IEEE.

关键词： Object detection

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LDWPSO based Bi-LSTM Model for Predicting the Missing Data in PHRs

LDWPSO based Bi-LSTM Model for Predicting the Missing Data i...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Prasad, Anoop N Gangambika, G. Nayana, P.C. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

In medical and health datasets, it's common for some information to be missing. When you look at datasets that are missing some information, you might get skewed results or less statistical power. Because of this, some data may be missing, which hurts the analysis of the data. These missing data should be replaced with appropriate values. This article suggests a way to estimate missing data using an optimised Bi-Directional Long Short-Term Memory (OBi-LSTM) applied to big data in healthcare. A memory gate is used in the suggested method to estimate missing data that comes up when collecting and processing data. In this study, information from the Korean National Health Nutrition Examination Survey (KNHNES) by the Korea Centers for Disease Control and Prevention (KCDC) is used. As typical South Korean health care records, they have a lot in common with personal health records (PHRs). On the basis of this, models can be made to estimate the missing data in PHRs. PHRs are also multimodal, which means that data can be collected from many different sources for a single object. The Bi-LSTM model is used in a multimodal setting because of this. By preprocessing, a KNHNES data set is made that has no missing values. In data set-based learning, a label is set as the first piece of data. The parameters of Bi-computational LSTM affect how fast the model works. Linearly decreasing weight particle swarm optimization is used in the research to deal with these problems (LDWPSO). There is a big difference in the number of parameters between the two models, but there isn't much difference in how accurate they are. A multi-modal approach can also cut down on the time it takes to process large amounts of data in places like hospitals and institutions. © 2023 IEEE.

关键词： Nutrition

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Enhanced AI Based Diabetic Risk Prediction Using Feature Scaled Ensemble learning Technique Based on Cloud Computing

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SN computer Science 2024年第8期5卷 1123页

作者： Manivannan, K. Ramkumar, K. Krishnamurthy, Revatthy Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Artificial Intelligence & amp Machine Learning Rajalakshmi Engineering College Tamilnadu Chennai India

Diabetes causes many health problems including microvascular disease, macrovascular abnormalities and neuropathy. Diabetes is one of the costliest diseases from an economic perspective, and most adults through diabetes live in small- and intermediate-revenue countries, creating additional financial burdens and problems for these *** healthcare industry is one ofvast industries in the comprehensivebudget with revenue and investment increasing every year. Early and exactanalysis of diabetes, especially in the initial stages of the disease, can be difficult for medical professionals. To resolve the issue, we deploy the AI based ensemble learning method. To attain the objective, we perform three stagesfor instance preprocessing, feature selection, and classification. In the first phase, we preprocess the diabetes dataset by using numeric normalization method. This method is used for the diabetes dataset improves the validity of subsequent ML progression and produces more reliable and descriptive results. After preprocessed the dataset we go to select the features based on Correlation-based feature selection (CFS) method. CFS aims to select a feature subset with strong predictive power while minimizing redundancy by estimating the correlation between features and target variables and cross-correlation between features. The proposed method needs to select the important features for classification. At last, we classify the diabetes dataset on the basis ofreviewed ensemble learning methodologies to progress prediction accurateness. A comparison of the review methods for diabetes risk prediction includes K-Nearest Neighbor (K-NN), Whale Optimization and Fuzzy Neural Network (WO-FNN), Decision Tree (DT), and Support Vector machine (SVM). In this paper, we use K-NN, WO-FNN and DT as the base models and SVM as the meta-model. In the ensemble learning methodology we examine that SVM is the best method for forecasting the diabetes at premature stages by the diabetes dat

关键词： Accurate diagnosis CFS Clinical data Diabetes Healthcare industry Numeric normalization method SVM

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Sample Complexity of Posted Pricing for a Single Item 38

Sample Complexity of Posted Pricing for a Single Item

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Billy Kesselheim, Thomas Ma, Will Singla, Sahil Cornell University School of Operations Research and Information Engineering United States University of Bonn Institute of Computer Science Lamarr Institute for Machine Learning and Artificial Intelligence Germany Columbia University Graduate School of Business Data Science Institute United States Georgia Tech School of Computer Science United States

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings. © 2024 Neural information processing systems foundation. All rights reserved.

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African Vultures Based Feature Selection with Multi-modal Deep learning for Automatic Seizure Prediction 2

African Vultures Based Feature Selection with Multi-modal De...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Sandhya, M. Khan, Zabiha Mohan, B.R. Nayana, C.P. Rajashekhar, S.A. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798350382952

The superiority of life for people with epilepsy can be greatly improved with the assistance of accurate seizure prediction and early warning. An automatic prediction model is required to procedure the EEG signals and account for the leads optimization problematic, as opposed to the majority of hand-designed prediction approaches. In this research, we put forth a fully automated model for seizure prediction using Channel and Spatial attention (CASA). The first step in the feature extraction practice is to pre-process the raw EEG signals. Large amounts of computation can be saved by adding more features to the system, but finding the right ones can be tricky. The African vulture optimization algorithm's (AVOA) strong capacity to break out of local optima is what makes this procedure possible. CASA saved the raw EEG data's temporal and geographical details. Automatic optimization of EEG full-lead data was completed with channel attention (CA), leading to an increase in the accuracy of predictions. The aforementioned adaptive learning of feature parameters was accomplished via spatial attention (SA). When all else fails, a fully associated layer is used to make the seizure forecast. The suggested algorithm is tested on the Freiburg EEG database, and the results reveal that the AVOA-based system performs admirably when it comes to predicting seizures. © 2024 IEEE.

关键词： Electroencephalography

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Uncertainty-induced Incomplete Multi-Omics Integration Network for Cancer Diagnosis

Uncertainty-induced Incomplete Multi-Omics Integration Netwo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Peipei Du, Ling Qiao, Sibo Yin, Nan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University School of Software Tianjin China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350386226

Cancer diagnosis with incomplete multi-omics data is inevitable since there are widely missing arbitrary omics in real-world applications. Currently, although much progress has been made in handling missing data, existing methods for cancer diagnosis still struggle to obtain credible predictions due to the relatively high uncertainty of missing omics data. In this paper, we propose a trusted and attention-based multi-omics integrated framework for cancer diagnosis using incomplete multi-omics data (TAIMONET). For the raw omics data, the framework first builds an attention-based shared encoder to pick out important features and align different omics data. Next, it utilizes generative adversarial learning to impute the missing omics features based on the extracted available omics features. Meanwhile, different reconstructed complete omics data are weighted and fused according to the attention mechanism. Moreover, it uses true-class-probability to calibrate the classification results and improve the reliability of the results. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed model are clearly validated. © 2024 IEEE.

关键词： Lung cancer

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Virtual Scribe for Visually Impaired Learners Through Interactive Speech-Based Application 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Sanjeetha, R. Peter, T. John Kallimani, Jagadish S. Sambhram Institute of Technology Bangalore India Department of Computer Science and Engineering Sambhram Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Blind people face various challenges every day, something that people take for granted. One such problem that blind students face is the need for a scribe to write their exam. The onus falls on the blind student to find themselves a scribe which in itself is a difficult task. But even if a blind student has found a scribe, the scribes come with their own set of problems, such as linguistic capability and illegible handwriting. To solve this problem, V-Scribe or virtual scribe is being proposed which is a web application that would completely replace a human scribe. The application uses speech recognition, text-to-speech and a natural language understanding module to understand the requests given by the blind student and give back an appropriate response. The DIET classifier developed by Rasa is trained, obtaining an accuracy of 96.7% for intent recognition and 94.7% for entity recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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