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Voice Mate: Voice Controlled Chessboard for Visually Impaired

Voice Mate: Voice Controlled Chessboard for Visually Impaire...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Abraham, Anishamol Mammen, Agnel Soyi, Ajay Jhonson, Ben Biju, Joel Amal Jyothi College of Engineering Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350382693

Traditional chess can affect people with disabilities who have physical difficulty controlling the pieces, although the concept and gameplay are closely related. This paper explores the concept of voice-controlled chessboards to address this gap and encourage participation in chess. This paper presents a method for creating voice-controlled chessboards. This method introduces the structure of the circuit board, focusing on communication skills. It explains how to use stepper motors to move the chess according to commands. The paper recognises the importance of careful observation and control to ensure correct pronunciation and good chess performance. This paper discusses possible implications and areas that require further consideration. These areas include ensuring speech accuracy, maintaining reliable status monitoring of the card, and ensuring motor control. Solving these issues through development and testing is crucial to the success of voice-controlled chessboards. The paper concludes by highlighting the importance of user testing with target users (people with disabilities) to improve usability. This project has the potential to not only make chess more accessible but also stimulate the development of new technologies for a variety of recreational games. © 2024 IEEE.

关键词： Stepping motors

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

引用

OCEANS 2024 - Halifax, OCEANS 2024

作者： Eccher, Davide Casari, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331540081

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker. © 2024 IEEE.

关键词： Underwater acoustics

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An Effective Pre-Owned Car Price Prediction Based on Multi Linear Regression Technique 1

An Effective Pre-Owned Car Price Prediction Based on Multi L...

引用

1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Kumar, Aman Singh, Dheeraj Chauhan, Keshav Guru Prasad, M.S. Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350341737

The market for the purchase and sale of second- hand automobiles has seen a trend towards expansion in recent years. Used vehicle buyers and sellers are able to conduct business at rates that are acceptable and fair thanks to scientific examination of automobile prices. This contributes to a reduction in the risks associated with conducting transactions in the used car market and encourages the market to continue to grow in a sustainable manner. Even though a lot of effort has been done on predicting the price of used cars, there are still a lot of obstacles standing in the way of an accurate and reliable estimate. Predictions of the prices of used automobiles that are accurate can assist individuals in making wise choices and avoiding the inflated costs of used vehicles on the market to the greatest extent possible. In this work multi linear regression technique is used, which is a type of supervised machine learning algorithm to build a model for predicting the price of pre-owned cars effectively. The dataset used for the prediction was collected from Kaggle webpage. After training and testing the model, the proposed model deployed using flask framework. The accuracy of the proposed model is 89.23% based on testing data. © 2023 IEEE.

关键词： Automobiles

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Deciphering Deception: Unmasking Fake News in Multilingual Contexts 5

Deciphering Deception: Unmasking Fake News in Multilingual C...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agarwal, Ashutosh Singh, Yash Pratap Rai, Vipin Galgotias University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

A major global worry in the ever-expanding digital ecosystem is the spread of incorrect information. The ability to recognize false news in non-English languages is still lacking, despite great advancements in the identification of false news in English. This research looks at the unique opportunities and problems that these languages bring. It offers a thorough assessment and comparative analysis of existing approaches, highlighting their advantages and disadvantages. By doing comparative study of various differ techniques we have found that Multilingual-Fake Model currently works the best. The results of the study highlight how critical it is to use inclusive and linguistically varied tactics in order to successfully counter disinformation. It emphasizes how crucial it is to create trustworthy tools and processes that can adjust to the subtleties of many languages and their socio-cultural settings. It highlights how crucial it is to comprehend these components completely in order to create detection methods that are more potent. The study admits that the dissemination of incorrect information is an issue that is deeply rooted in the social environment of linguistic groups and transcends language borders. A discussion of possible future study areas closes the report. In a multilingual culture, this study marks a substantial breakthrough toward the development of more thorough and efficient techniques for spotting bogus news. © 2024 IEEE.

关键词： Bidirectional Transformer fake news multi-lingual

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Augmenting Sentiments into Chat-GPT Using FacialEmotion Recognition 10

Augmenting Sentiments into Chat-GPT Using FacialEmotion Reco...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Iyer, Aditya A. Vojjala, Saipranav Andrew, J. Manipal Institute of Technology Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384369

This research initiative addresses the task of enhancing Chat Generative Pre-trained Transformer's (ChatGPT's) conversational capabilities by integrating the comprehension and response to user emotions conveyed through facial expressions. The central challenge lies in refining the AI system's proficiency to tailor responses according to users' detected emotional states. In this pursuit, our study adopts a comprehensive approach, aiming to seamlessly incorporate emotional intelligence into AI-driven interactions. To achieve this objective, the methodology involves integrating real-time sentiment analysis based on facial expressions into the ChatGPT framework. This is carried out through the utilization of a deep convolutional neural network (DCNN) architecture, designed to recognize and interpret various emotions exhibited in facial expressions. The primary goal is to enable ChatGPT to dynamically adjust its responses, fostering a more empathetic and contextually relevant interaction with users. In terms of evaluation metrics for facial expression recog- nition, our assessment employs a confusion matrix to quantify the model's performance across different emotional categories. Additionally, a heuristic approach is implemented, wherein the sum total probability of each detected emotion is calculated over the duration the user enters the prompt. These evaluation methodologies aim to provide a comprehensive understanding of the model's accuracy and effectiveness in discerning and responding to user emotions. Overall, this research contributes to the ongoing endeavor of imbuing AI systems with emotional intelligence, paving the way for more nuanced and human-like interactions. © 2024 IEEE.

关键词： Chatbots

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

引用

32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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A CNN Based Ensemble Deep Learning Approach for Melanoma Detection

A CNN Based Ensemble Deep Learning Approach for Melanoma Det...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Mullick, Mohammad Abu Obaida Khan, Nafi Sadman Fahid, Ahnaf Tahmid Mahmud, Mahin Rahman, Raiyan United International University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350364866

Melanoma, the most aggressive form of skin cancer, necessitates early detection for successful treatment. This paper proposes an ensemble Convolutional Neural Networks (CNNs) approach to classify dermoscopic images of Melanoma (MEL) and Melanocytic Nevi (NV), by systematically selecting and comparing various pre-trained models with different optimizers and preprocessors through trial and error, as well as optimizing loss functions. The ensemble method takes predictions from multiple pre-trained models and selects the most reliable outcome using a custom metric. The result presents an optimal CNN ensemble model built of DenseNet121 and VGG16 with cropping and scaling, contrast enhancing, and exposure adjusting as image preprocessors that achieved a notable accuracy of 97.5% on a customized and balanced version of the Kaggle HAM10000 dataset. This approach aims to improve patient outcomes by providing a valuable tool for early melanoma detection. © 2024 IEEE.

关键词： Convolutional neural networks

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The Tree Autoencoder Model, with Application to Hierarchical data Visualization 38

The Tree Autoencoder Model, with Application to Hierarchical...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Carreira-Perpiñán, Miguel Á. Gazizov, Kuat Dept. of Computer Science and Engineering University of California Merced United States

We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and of PCA in its leaves makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing Agricultural Quality Control: Freshness Detection in Root Vegetables Through Deep Neural Networks 15

Enhancing Agricultural Quality Control: Freshness Detection ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shorna, Aklema Akther Ave, Abida Sultana Mahi, Hasibul Hasan Chowdhury Shital, Sabrina Bari Sowrna, Nourin Jahan Nahid, Aynul Hasan Daffodil International University Dept. computer science and engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

In monarchy agricultural technology, this research pioneers the classification of freshness in root vegetables, a field that has been abruptly neglected in earlier research on a variety of vegetables. This study addresses this research gap and employs a transfer learning-based system to swiftly determine the freshness of root vegetables through images. To conduct this research, we create our own dataset which contains six different vegetables, 'Potato', 'Radish', 'Carrot', 'Onion', 'Taro', and 'Turnip'. Vegetables were collected from the local market and clicked manually by personal mobile phone, giving the dataset authenticity and real-world application. In total we collect 6384 images, divided into 3 classes: 'Fresh', 'Aged' and 'Rotten' based on their physical attributes and texture. Different types of transfer learning model VGG19, ResNet50, InceptionResNetV2 and DenseNet201 used to classify freshness of root vegetables. Although every model achieved decent accuracy, DenseNet201 achieved a remarkable result. DenseNet201achived highest accuracy 98.75% where data loss was 0.02%. Conversely, though ResNet50 achieved lowest accuracy 60.97% among all models. The variety of algorithms used seeks to determine the best course of action for this particular agricultural setting in addition to precisely classifying freshness. This research not only defines a novel benchmark but also sets the stage for future explorations in agricultural technology, ushering in a new era of precision farming for root vegetables © 2024 IEEE.

关键词： classification deep neural network freshness Root vegetable transfer learning

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tanaka, Yuuri Hashimoto, Yasuhiro The University of Aizu Dept. Computer Science & Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350362480

Human beings have long been regarded as "homo narrans,"employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling"ability in humans, who adjust readability based on the original story's readability. These insights advance our understanding of the "AI narrans"concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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