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Computing and Machine Intelligence (ICMI), International Conference on

作者： Jared Romeo Mahmoud Abbass Ahmed Sherif Mohammad M. R. Khan Mamun Mohamed Elsersy Kasem Khalil School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA Computer and Information Systems Dept. Higher Colleges of Technology AlAin Abu Dhabi UAE Electrical and Computer Engineering Department University of Mississippi MS USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

As more information about people's health is gath-ered and analyzed, privacy concerns have become increasingly essential. This paper summarizes how machine learning (ML) methods are presently used to safeguard privacy in e-health apps. Furthermore, we discuss how privacy-preserving approaches such as differential privacy, secure multi-party computing, and homomorphic cryptography may be utilized to protect privacy in ML. The study also conducts an overview of the literature on how these methods can be used in various e- health scenarios, such as predictive modeling, disease detection, and clinical decision support. Based on the privacy-preserving technique, we classified current privacy-preserving ML work into four categories (i.e., data encryption, data anonymization, model encryption, and model obfuscation). Finally, the article discusses the challenges of applying privacy-preserving machine learning (PPML) in the e-health domain and what types of research could be conducted as open future problems in this field.

关键词： Surveys Privacy Computational modeling Machine learning Medical services Predictive models Data models

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Proposing a Low-Cost, Transportable Horizontal Binaural Test Using Headphones

Proposing a Low-Cost, Transportable Horizontal Binaural Test...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Mohsen Sheikh Hassani James Green Rafik Goubran Frank Knoefel Neil Thomas Dept. of Systems & Computer Engineering Carleton University Ottawa Canada Dept. of Systems & Computer Engineering Carleton University Bruyère Research Institute Ottawa Canada Elisabeth-Bruyère Hospital Bruyère Research Institute Ottawa Canada Department of Medicine Elisabeth-Bruyère Hospital Bruyère Research Institute University of Ottawa Ottawa Canada

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Binaural hearing plays a significant role in auditory perception, spatial awareness, and sound source differentiation. Studies have linked cognitive decline, traumatic brain injuries, and neurodegenerative disease with decline in binaural performance, and quantification thereof may provide key information related to early onset of such diseases. Current horizontal binaural tests require multiple external speakers and an anechoic chamber, preventing broad clinical deployment, especially in remote communities. Furthermore, they use design parameters that differ widely. We hereby aim to develop a portable, easy-to-perform binaural hearing performance test, as well as identifying the ideal design parameters used in current literature, including audio prompt type, frequency, duration, and modality (speaker vs. headphone). Results indicate that a voice-form audio, with a sampling frequency of 1 kHz combined with a 4-second duration yields the best outcomes. Moreover, comparisons between speaker modality tests and our proposed headphone modality test using a Head Related Transfer Function (HRTF) reveal a high level of agreement between performances. Notably, the proposed headphone test mitigates sources of bias such as informed guessing due to visual cues, memorization of source locations, and movement of the head during tests. The findings establish the potential of a low-cost, easily accessible binaural performance test applicable across diverse settings. This research contributes insights into the design and implementation of binaural tests, with implications for fields such as audiology and neuroscience.

关键词： Headphones Location awareness Visualization Noise Transfer functions Auditory system Position measurement

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Lightweight Spiking Neural Network Based Detector for Interweave Cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

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IEEE International Conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

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Dense Residual Networks for Gaze Mapping on Indian Roads

arXiv

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arXiv 2022年

作者： Kapoor, Chaitanya Kumar, Kshitij Vishnoi, Soumya Ramanathan, Sriram Dept. of Electrical and Electronics Engineering Bits Pilani Dept. of Computer Science and Information Systems Bits Pilani

In the recent past, greater accessibility to powerful computational resources has enabled progress in the field of Deep Learning and computer Vision to grow by leaps and bounds. This in consequence has lent progress to the domain of Autonomous Driving and Navigation systems. Most of the present research work has been focused on driving scenarios in the European or American roads. Our paper draws special attention to the Indian driving context. To this effect, we propose a novel architecture, DR-Gaze, which is used to map the driver's gaze onto the road. We compare our results with previous works and state-of-the-art results on the DGAZE dataset. Our code will be made publicly available upon acceptance of our paper. © 2022, CC BY.

关键词： Navigation systems

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Activity Recognition from Channel State Information for Few-Sampled Scenarios

Activity Recognition from Channel State Information for Few-...

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IEEE Globecom Workshops

作者： Guillermo Diaz Iker Sobron Iñaki Eizmendi Iratxe Landa Manuel Velez Dept. of Communications Engineering University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Computer Languages and Systems University of the Basque Country (UPV/EHU) Bilbao Spain

Human Activity Recognition (HAR) using channel state information (CSI) from wireless systems has been a field in progress for the last few years. In particular, amplitudes of WiFi-based CSI have been widely used for HAR in indoor scenarios. In contrast, CSI phases are less exploited due to higher sensitivity to timing errors in channel estimation. This paper further explores a recent phase processing method to obtain environmental information from CSI and improves classification using a Convolutional Neural Network in few-sampled scenarios. For this purpose, a new dataset has been generated with measurements of three receivers on different days and with different people, for activity recognition and people counting. In addition, we incorporate a transfer learning strategy using fine-tuning along with a data fusion model from different receivers to improve accuracy metrics by training the model and validating it on data taken on different days. Promising results with accuracies higher than 80 % have been obtained with transfer learning between days using 5 % of data for fine-tuning.

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Online Canonical Correlation Analysis via Rayleigh-Ritz Projections

Online Canonical Correlation Analysis via Rayleigh-Ritz Proj...

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Asilomar Conference on Signals, systems & computers

作者： Vassilis Kalantzis Panagiotis A. Traganitis Charilaos I. Kanatsoulis IBM Research MIT-IBM Watson AI Lab Yorktown Heights NY USA Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Canonical correlation analysis (CCA) is a classical statistical tool that enables processing of multiview data in a plethora signal processing, machine learning and data mining applications, by identifying a common linear subspace from the available data views. Most algorithms tackling the CCA task require all the data per view to be available. Nevertheless, in many cases, data are not available in batch form and may arrive in streaming fashion. This work puts forth a novel, computationally efficient, projection based method for identifying and updating the common subspace on-the-fly, as new data arrive, while retaining its' fidelity. Preliminary numerical tests, on synthetic and real data benchmarks, showcase the potential of the proposed method.

关键词： computers Correlation Machine learning algorithms Signal processing algorithms Machine learning Signal processing Approximation algorithms Performance analysis Computational efficiency Data mining

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EfficientNetB0-based Rice Disease Classification: A Strategic Fine-Tuning Approach

EfficientNetB0-based Rice Disease Classification: A Strategi...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shivani Mishra Chavali Sai Sreeram Yadav Akansha Raju A Balasundaram Ayesha Shaik Dept. of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Center for Cyber Physical Systems Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Rice, a major stape food, is crucial for both sustenance and the living of millions of farmers. Detecting rice leaf diseases is vital to prevent widespread crop losses, safeguarding farmers’ incomes and global food supply. While current detection systems face accuracy and robustness challenges, deep learning models, particularly the EfficientNetB0, show promise. Despite their sensitivity to noise and variations in leaf appearance, ongoing research addresses these issues. This study introduces the use of EfficientNetB0 for accurate classification, incorporating a fine-tuning strategy in a brief five-epoch training period. This approach optimally balances model adaptation and prevents overfitting, leveraging the deep neural network’s ability to capture complex hierarchical features. The limited fine-tuning duration addresses potential risks while maximizing the model’s robust features. This unique amalgamation of advanced architecture and tailored training strategy aims to achieve efficient and accurate disease classification. The research signifies a step toward revolutionizing rice disease detection, emphasizing the importance of striking a balance between cutting-edge methodologies and practical considerations in the pursuit of agricultural sustainability. Before Fine Tuning the Training Accuracy was $\mathbf{9 8. 4 6 \%}$, Validation Accuracy was $\mathbf{9 9. 1 5 \%}$ and Test Accuracy was $\mathbf{9 8. 4 4 \%}$.After Fine Tuning there was a significant increase in the accuracy. The Training Accuracy came to be $\mathbf{9 9. 7 7 \%}$, Validation Accuracy is $\mathbf{9 9. 4 3 \%}$ and Test Accuracy: 99.12%

关键词： Training Adaptation models Accuracy Sensitivity Computational modeling Noise Robustness Sustainable development Tuning Diseases

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Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Alvin Steven Suharnawi Farrikh Alzami Filmada Ocky Saputra Rama Aria Megantara Pulung Nurtantio Andono Dept. of Information Systems Universitas Dian Nuswantoro Indonesia Dept. of Information Engineering Universitas Dian Nuswantoro Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools &automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance.

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Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： R. Venkatesan M. Sangeetha S. Nivedha M. Selvakumar K.S. Chandru P. Parthasarathi Department of Information Technology Bannari Amman Institute of Technology Erode India Department of Information Technology K S Rangasamy College of Technology Namakkal India Dept. of Computer Science & Engineering Hindusthan College of Engg and Technology Coimbatore India Dept. Computer Science & Business Systems Bannari Amman Institute of Technology Erode India Dept. Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Sensitivity Machine learning algorithms Reviews Social networking (online) Biological system modeling Long short term memory

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Balanced Spherical Grid for Egocentric View Synthesis

Balanced Spherical Grid for Egocentric View Synthesis

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Changwoon Choi Sang Min Kim Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Korea Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coordinate instead of conventional Cartesian coordinate. Cartesian feature grid is inefficient to represent large-scale unbounded scenes because it has a spatially uniform resolution, regardless of distance from viewers. The spherical parameterization better aligns with the rays of egocentric images, and yet enables factorization for performance enhancement. However, the naive spherical grid suffers from singularities at two poles, and also cannot represent unbounded scenes. To avoid singularities near poles, we combine two balanced grids, which results in a quasi-uniform angular grid. We also partition the radial grid exponentially and place an environment map at infinity to represent unbounded scenes. Furthermore, with our resampling technique for grid-based methods, we can increase the number of valid samples to train NeRF volume. We extensively evaluate our method in our newly introduced synthetic and real-world egocentric 360 video datasets, and it consistently achieves state-of-the-art performance.

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