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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Cherian, Aswathy K. Devipriya, S. Saoji, Bhushan P. Mallikeswari, B. Thiagarajan, R. Krishnamoorthy, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Jerusalem College of Engineering Department of Cse Chennai India Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navimumbai India JusticeBasheer Ahmed Sayeed College For Women Dept of Computer Chennai India Prathyusha Engineering College Department of It Tamilnadu Chennai India Chennai Institute of Technology Centre For Advanced Wireless Integrated Technology Chennai India

ISBN: (纸本)9798350353068

We often convey our happiness via smiling or making a happy face. Simple, latent feelings like contentment and joy are brought to the surface in this way. In terms of social communication, this is the most difficult and potent task there is. One function of digital cameras is smile recognition, which prevents taking pictures of people unless they are smiling. There are two distinct stages to smile detection. After identifying a face in a video or picture, it watches for a grin. To be more precise, we may state that a motion detector takes a video or image and breaks it down into frames. It then examines the frameworks, such as the balanced level and flash, for the face area. The camera can tell whether someone is laughing or smiling because it looks for certain facial features - a jutted jaw, narrowed eyes, and an elevated cheekbone among others. The components of computer architecture and software advancements are advancing at a rapid pace, which is leading to a progressive increase in customer needs for electric products. A growing number of people are interested in human computer interaction systems that differ from the conventional user interface, which includes the keyboard and mouse. This article depicts the realtime implementation of deep learning algorithms in computer settings with limited resources. In this study, we provide HLSR, a new kind of deep learning model that combines the XGBoost and Convolutional Neural Network (CNN) algorithms for smile recognition. To get a clear picture of how well the suggested technique works, we cross-validate the HLSR model with the current CNN model. © 2024 IEEE.

关键词： Face recognition

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Statistical Analysis for Air Quality Assessment and Evaluation: A Data Mining Approach 9

Statistical Analysis for Air Quality Assessment and Evaluati...

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9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Mangla, Monika Sharma, Nonita Mehta, Vaishali Mohanty, Sachi Nandan Saxena, Komal Tilak College of Engineering Dept. of Computer Engineering Lokmanya Navi Mumbai India Dr. B.R Ambedkar National Institute of Technology Dept. of Computer Science and Engg. Jalandhar India Dept. of Computer Science and Engineering Hyderabad India Amity Institute of Information Technology Amity University Uttar Pradesh India

ISBN: (纸本)9781665417037

In this era of the industrial revolution, air Pollution is emerging as the most concerning problem specifically in developing countries. This air pollution considers the escalation of several pollutants in the air like nitrogen oxides, sulfur dioxide, carbon dioxide, carbon monoxide and particulate matter (PM), CFCs (Chlorofluorocarbons), odors, and radioactive pollutants, etc. The rise of these pollutants in the air is putting a significant impact on the health of mankind and animals in the affected area. In this manuscript, the authors aim to analyze the quality of air in terms of SO2 and NO2 emissions. Further, an analysis is also made regarding the amount of PM in the air in terms of Reparable Suspended Particulate Matter (RSPM) and Suspended Particulate Matter (SPM). To perform this survey, the authors consider the air quality of various states in India. Among the various states considered for the survey, authors consider Chandigarh and Delhi for further analysis. © 2021 IEEE.

关键词： Air quality

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Enhancing Ophthalmic Diagnostics: CNNs in Cataract Detection

Enhancing Ophthalmic Diagnostics: CNNs in Cataract Detection

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Disruptive Technologies (ICDT), International Conference on

作者： Kartik Solanki Shaurya Pratap Singh Aman Yadav Vikrant Sharma Shashank Awasthi Satvik Vats Dept. of Computer Science Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science and Engineering G.L. Bajaj Institute of Management Greater Noida

Cataracts have been one of the most prevalent eye disorders, which often can cause significant visual impairment due to the clouding of the eye's lens. This condition can worsen in future, often leading to severe vision problems and even blindness. Consequently, detecting cataracts is paramount in mitigating the associated risks and preventing the onset of blindness. Throughout the years there has been quite good progress in leveraging cutting-edge technology, especially due to machine learning, to improve the perfection and efficacy of cataract detection. Convolutional Neural Networks (CNNs) have made an appearance as a commanding implement for computerized the systematization of eye images in the context of cataract identification. Proposed research was more concerned with fine-tune the process of cataract identification, aiming to increase the accuracy while minimizing the data loss. To accomplish this, we conducted a series of experiments, with a key focus on manipulating a critical parameter: the number of training epochs. The proposed research revealed a compelling relationship between the number of training epochs and the accuracy and loss of data in CNN. As we delved into a spectrum of epoch values, a clear pattern emerged: the higher the number of epochs, the more refined and potent the model became. In this research, a significant milestone was reached when utilizing a generous number of 90 epochs, resulting in an impressive accuracy rate of 97.37%.

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On the Parallelizability of Approval-Based Committee Rules

arXiv

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arXiv 2025年

作者： Fitzsimmons, Zack Hassan, Zohair Raza Hemaspaandra, Edith Dept. of Math. and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Computer Science Rochester Institute of Technology RochesterNY14623 United States

Approval-Based Committee (ABC) rules are an important tool for choosing a fair set of candidates when given the preferences of a collection of voters. Though finding a winning committee for many ABC rules is NP-hard, natural variations for these rules with polynomial-time algorithms exist. The Method of Equal Shares, an important ABC rule with desirable properties, is also computable in polynomial time. However, when working with very large elections, polynomial time is not enough and parallelization may be necessary. We show that computing a winning committee using these ABC rules (including the Method of Equal Shares) is P-hard, thus showing they cannot be parallelized. In contrast, we show that finding a winning committee can be parallelized when the votes are single-peaked or single-crossing for the important ABC rule Chamberlin-Courant. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

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IoT-based Precision Agriculture Monitoring System: Enhancing Agricultural Efficiency

IoT-based Precision Agriculture Monitoring System: Enhancing...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Tahmina Akter Tanjim Mahmud Rishita Chakma Nippon Datta Mohammad Shahadat Hossain Karl Andersson Dept. of CSE Port City International University Chittagong Bangladesh Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of CSE Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of CSE University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

This research study introduces an IoT-based Agricultural Monitoring System designed to enhance precision farming practices. Employing Arduino microcontrollers and a network of sensors including water level, soil moisture, temperature, and pH, the system enables real-time monitoring of agricultural parameters. Integration with GSM and WiFi modules facilitates data communication and remote-control capabilities. Additionally, solar power integration enhances sustainability. The collected data is uploaded to a cloud platform for analysis, providing farmers with actionable insights for informed decision-making. The study aims to optimize resource utilization, improve crop yield, and promote sustainable agriculture practices.

关键词： Temperature sensors Precision agriculture Soil moisture Sensor systems Real-time systems Sensors Resource management

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Classifying Temporomandibular Disorder and Trigeminal Neuralgia using Self-Supervised and Deep Learning Approach

Classifying Temporomandibular Disorder and Trigeminal Neural...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Swati Saigaonkar Vaibhav Narawade Dept. of Computer Engineering Ramrao Adik Institute of Technology D Y Patil Deemed to be University Nerul India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The study explores the challenges of accurately diagnosing Temporomandibular Disorder (TMD) and Trigeminal Neuralgia (TN), two conditions that often result in severe facial pain. Proper assessment of pain characteristics is essential to avoid misdiagnosis and ensure effective treatment for these conditions, and clinical notes play a significant role in distinguishing between these disorders as they contain rich information. The model was initially trained using a selfsupervised learning approach, where descriptions of both TMD and TN were fed using a Masked Language Modeling (MLM) technique. A custom deep learning model was built. This approach was chosen because the quantity of clinical notes from the MIMIC-III database alone was insufficient to train a model capable of precisely distinguishing between the two diseases. The accuracy obtained is significantly high with a value of 0.98 as against fine-tuned baseline model DISTILBERT. For rare diseases, often the problem data scarcity arises which could be tackled using the suggested approach.

关键词： Deep learning Accuracy Pain Databases Computational modeling MIMICs Market research Diseases

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A Comparative Study on Predicting Food Quality using Machine Learning Techniques 7

A Comparative Study on Predicting Food Quality using Machine...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Anly Antony, M. Kumar, R. Satheesh Sahrdaya College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Thrissur India

ISBN: (纸本)9781665405201

Machine Learning has been established to be a state of the art technology with a high number of successful cases in various research areas. These techniques have been applied widely on food quality evaluation in recent years. This paper reviews the recent developments in the field of estimating food quality and food safety using machine learning techniques. The primary focus is on the supply of defect-free, premium quality products to the consumers which is considered to be major factor for the competitiveness of manufacturing units. This paper provides a brief description of machine learning techniques and then a detailed comparative study of different techniques to grade different kinds of foods. We investigated many articles to resolve the problems in food domain which includes detecting the quality of fruits, vegetables, seafood, meat and dairy products. The results of this survey shows that machine learning techniques outperforms the conventional methods used in the food domain. © 2021 IEEE.

关键词： Grading

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Machine Learning Approaches To Predict The Stability of Smart Grid

Research Square

引用

Research Square 2024年

作者： Satu, Shahriare Khan, Imran Dept. of Management Information Systems Noakhali Science and Technology University Noakhali3814 Bangladesh Dept. of Computer Science and Engineering Gono Bishwabidyalay Dhaka1344 Bangladesh

The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry. © 2024, CC BY.

关键词： Smart power grids

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Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments

Learning Bayes-Optimal Channel Estimation for Holographic MI...

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IEEE International Conference on Communications (ICC)

作者： Wentao Yu Hengtao He Xianghao Yu Shenghui Song Jun Zhang Ross D. Murch Khaled B. Letaief Dept. of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Dept. of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.

关键词： Training 6G mobile communication Machine learning algorithms Simulation Channel estimation Estimation Machine learning

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Deep Learning-Based Adaptive Joint Source-Channel Coding using Hypernetworks

Deep Learning-Based Adaptive Joint Source-Channel Coding usi...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Songjie Xie Hengtao He Hongru Li Shenghui Song Jun Zhang Ying-Jun Angela Zhang Khaled B. Letaief Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Dept. of Information Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for the next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained under specific channel conditions. In this paper, we propose a generic framework for channel-adaptive DJSCC by utilizing hypernetworks. To tailor the hypernetwork-based framework for communication systems, we propose a memory-efficient hypernetwork parameterization and then develop a channel-adaptive DJSCC network, named Hyper-AJSCC. Compared with existing adaptive DJSCC based on the attention mechanism, Hyper-AJSCC introduces much fewer parameters and can be seamlessly combined with various existing DJSCC networks without any substantial modifications to their neural network architecture. Extensive experiments demonstrate the better adaptability to channel conditions and higher memory efficiency of Hyper-AJSCC compared with state-of-the-art baselines.

关键词： Adaptive systems Wireless networks Soft sensors Image communication Neural networks Memory management Network architecture

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