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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Jain, Shweta Aggarwal, Rishit Gandhi, Anju Bhandari Department of Artificial Intelligence and Machine Learning Panipat Institute of Engineering and Technology Panipat India Department of Computer Science and Engineering Graphic Era University Dehradun India

ISBN: (纸本)9781839539848

The Internet of Things (IoT), which is building a new world where everything is interconnected and can be remotely operated, is one of the developing technologies. The epoch of machines conquering the universe is just getting warmed up. The next technological innovation is blockchain, which uses a peer-to-peer communication link to preserve general transactional data and information (or blocks) across several repositories (or chains). The terminology "Digital Ledger" alludes to an instance of block storage. Quantum computing is the next quickly developing technology. It incorporates the use of numerous quantum physics aspects, notably superposition and entanglement. Every foundational premise of the computational game is amended by quantum excellence. A new paradigm dubbed Quantum - Chain of Things (QCoT), which is the most potent technology from the sub-emerging technologies is presented when we scrutinize the collaboration of the three powerful advanced. The BC-IOT (Blockchain coupled with IOT leveraging Arduino) and QC-IOT (Quantum computing merged with IOT using IBM Qiskit) subsystems are the cornerstones of QCoT technology. The prevailing breakthroughs in blockchain, quantum computing, and IOT will be addressed in this paper, followed by an exploration of the recommended architectural model for the improved technology. © The Institution of Engineering & Technology 2023.

关键词： Blockchain

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Exploiting VLM Localizability and Semantics for Open Vocabulary Action Detection

Exploiting VLM Localizability and Semantics for Open Vocabul...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Bao, Wentao Li, Kai Chen, Yuxiao Patel, Deep Min, Martin Renqiang Kong, Yu Michigan State University Department of Computer Science and Engineering United States Nec Laboratories America Machine Learning Department United States Rutgers University Departruent of Computer Science United States

ISBN: (纸本)9798331510831

Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos from a fixed set of action categories. However, this constrained setting is not viable in an open world where test videos inevitably come beyond the trained action categories. In this paper, we address the practical yet challenging Open-Vocabulary Action Detection (OVAD) problem. It aims to detect any action in test videos while training a model on a fixed set of action categories. To achieve such an open-vocabulary capability, we propose a novel method OpenMixer that exploits the inherent semantics and localizability of large vision-language models (VLM) within the family of query-based detection transformers (DETR). Specifically, the OpenMixer is developed by spatial and temporal OpertMixer blocks (S-OMB and T-OMB), and a dynamically fused alignment (DFA) module. The three components collectively enjoy the merits of strong generalization from pretrained VLMs and end-to-end learning from DETR design. Moreover, we established OVAD benchmarks under various settings, and the experimental results show that the OpenMixer performs the best over baselines for detecting seen and unseen actions. We release the codes, models, and dataset splits at https://***/Cogito2012/0penMixer. © 2025 IEEE.

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A Comprehensive Food Identification and Waste Reduction Solution with Built-in Nutritional Tracking using machine learning 8

A Comprehensive Food Identification and Waste Reduction Solu...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jonathan, J. Benjamin, Ruth Moly Prasad, Gunji Prem Karunya Institute of Technology and Sciences Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Food Solutions and Nutrition Checker is an innovative and comprehensive project aimed at providing users with a reliable tool to identify various foods and dishes, while also offering a custom-built database for detecting fresh and wasted food items. With this system, users can easily determine the nutritional value of different foods, making informed decisions about their dietary choices. This study incorporates advanced technology that enables the recognition of a wide range of food items. By simply capturing an image or specifying the dish, users can obtain accurate information about the food, including its ingredients and origin. This feature is particularly helpful for individuals with specific dietary needs or restrictions, allowing them to easily identify suitable menu options. Additionally, this study includes a custom-built data system that helps to identify fresh and wasted food. By analyzing factors such as expiration dates, storage conditions, and quality indicators, the proposed system can determine the freshness status of various food items. This capability promotes food waste reduction by enabling users to make informed decisions regarding the consumption or disposal of perishable goods. Moreover, this study incorporates a nutritional tracker that provides users with comprehensive information regarding the nutritional composition of different foods. This feature includes details such as calorie count, macronutrient breakdown, vitamins, and minerals, empowering users to make healthier food choices aligned with their dietary goals and requirements. Overall, the proposed model offers a holistic approach for food identification, waste reduction, and nutritional tracking. By incorporating advanced technology and a robust database, this study aims to facilitate informed decision-making and promote healthier dietary practices. © 2024 IEEE.

关键词： Food ingredients

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Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

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Intelligent Automation & Soft Computing 2023年第3期35卷 3545-3555页

作者： C.P.Thamil Selvi P.Muneeshwari K.Selvasheela D.Prasanna Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatore641009TamilnaduIndia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai602105Tamil NaduIndia Department of Computer Science and Engineering Sreesakthi Engineering CollegeKaramadai641104Tamil NaduIndia Department of Computer Science and Engineering Mahendra Engineering CollegeNamakkal637503Tamil NaduIndia

The term sentiment analysis deals with sentiment classiﬁcation based on the review made by the user in a social *** sentiment classiﬁcation accuracy is evaluated using various selection methods,especially those that deal with algorithm *** this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and *** order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andﬁnally produces the results as informative or *** challenge is to convert non-informative into informative using the concepts of classiﬁers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector machine(SVM)and decision *** work also addresses simulated annealing along with QER to classify data based on sentiment *** the input volume is very fast,the work also addresses the concept of big data for information retrieval and *** result com-parison shows that the QER algorithm proved to be versatile when compared with the result of *** work uses Twitter user comments for evaluating senti-ment analysis.

关键词： Sentiments QER SVM bayes multinomial big data analysis simulated annealing

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Node Coverage and Connectivity Optimization in Heterogeneous Wireless Sensor Networks Using Improved Flower Pollination Algorithm 4

Node Coverage and Connectivity Optimization in Heterogeneous...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mahankali, Saritha Kesavan, R. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Vardhaman College of Engineering Department of Computer Science & Engineering Telangana India Rajalakshmi Engineering Department of Artificial Intelligence & Machine Learning Chennai India

ISBN: (纸本)9798331529635

Wireless sensor networks (WSNs) rely on energy-conserving routing protocols due to the limited power and communication capabilities. The LEACH protocol has seen extensive usage despite its homogeneous network foundation, leads inadequate signal coverage. To solve the issue, this research proposes an improved flower pollination algorithm (IFPA) improves flower pollination algorithm (FPA) addresses issues with slow convergence and local optima. This research presents an enhanced clustering technique that may be utilized in heterogeneous sensor networks. Its main feature is the ability to adjust the status of data transmission. Once the observed information intensity above the predefined threshold, the data transmission connection is initiated and data is sent to the sink by the cluster heads, which filter the perceived information. Otherwise, cluster nodes keep track of the data and wait for the next batch of nodes to provide data. The good news is that the simulation results show a network lifespan that is several times longer than the LEACH protocol. The IFPA method stands out because it increases coverage rates while drastically decreasing the cost of deploying networks. As a result, the suggested method might find many uses, helping to lower costs and improve the performance of wireless sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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Predicting Indian Sign Language Gestures Using Deep learning and machine learning Techniques 4

Predicting Indian Sign Language Gestures Using Deep Learning...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Fulkar, Bhushan Burle, Rushikesh Mahale, Pramod Pacaharaney, Utkrsha Datta Meghe institute of Higher Education and Research Department of Artificial intelligence And Data science Wardha India Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India Datta Meghe institute of Higher Education and Research Department of Artificial Intelligence And Machine Learning Wardha India

ISBN: (纸本)9798331540364

Indian Sign Language is the language used by people with disabilities in India. Unfortunately, the general public's ignorance of sign language makes it difficult for the especially abled and the general public to communicate. We are releasing a strategy to reduce this difference. We have adopted two distinct strategies to deal with this problem. first by employing the depth+RGB data acquired with a Microsoft Kinect to make gesture predictions in real-time. To segment the hand region from the RGB-D camera data, we used affine transformation and 3D reconstruction to map the RGB and depth information. Convolutional neural networks were trained with inputs that were segmented hand images or videos. With 50 static Indian Sign Language hand gestures used for training, a 97.18% classification accuracy was achieved on test data. Furthermore, this model achieved a 97.71 % classification accuracy when we transferred learning American Sign Language. A convolutional kernel and an LSP were used to train twenty dynamic gestures. This model's classification accuracy was 98.88%. Nevertheless, as soon as we implemented this system, we found that the methodology was flawed. Essentially, it is absurd to need to carry around both a bulky Microsoft Kinect and a device capable of performing the computation required for human-to-person interaction. We attempted to address this issue by using semantic segmentation of the hands. U-Net and ResNet 101 were combined. © 2024 IEEE.

关键词： Affine transforms

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CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments 30

CLEVR-POC: Reasoning-Intensive Visual Question Answering in ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Abraham, Savitha Sam Alirezaie, Marjan De Raedt, Luc Australian Institute for Machine Learning The University of Adelaide Australia Flybits Labs. TMU Creative AI Hub Toronto Canada Örebro Sweden Department of Computer Science KULeuven Belgium

ISBN: (纸本)9782493814104

The integration of learning and reasoning is high on the research agenda in AI. Nevertheless, there is only a little attention to use existing background knowledge for reasoning about partially observed scenes to answer questions about the scene. Yet, we as humans use such knowledge frequently to infer plausible answers to visual questions (by eliminating all inconsistent ones). Such knowledge often comes in the form of constraints about objects and it tends to be highly domain or environment-specific. We contribute a novel benchmark called CLEVR-POC for reasoning-intensive visual question answering (VQA) in partially observable environments under constraints. In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged to generate plausible answers to questions about a hidden object in a given partial scene. For instance, if one has the knowledge that all cups are colored either red, green or blue and that there is only one green cup, it becomes possible to deduce the color of an occluded cup as either red or blue, provided that all other cups, including the green one, are observed. Through experiments, we observe that the low performance of pre-trained vision language models like CLIP (≈ 22%) and a large language model (LLM) like GPT-4 (≈ 46%) on CLEVR-POC ascertains the necessity for frameworks that can handle reasoning-intensive tasks where environment-specific background knowledge is available and crucial. Furthermore, our demonstration illustrates that a neuro-symbolic model, which integrates an LLM like GPT-4 with a visual perception network and a formal logical reasoner, exhibits exceptional performance on CLEVR-POC. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge management

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Sentiment Analysis on Real-Time Twitter Data Using LSTM with Mutually Inclusive Classifiers 8th

Sentiment Analysis on Real-Time Twitter Data Using LSTM with...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Soumya, B.J. Swetha, B.N. Meeradevi, Ak. Kanavalli, Anita Shubhangi Singh, Yashashwini Anuritha, L. Singh, Anushka Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819707430

A novel approach to perform sentiment analysis on real-time Twitter data using long short-term memory (LSTM) neural networks with mutually inclusive classifiers. The work leverages the vast amount of publicly available Twitter data, enabling us to analyze the sentiments expressed by users in real-time. The Twitter data by cleaning, tokenizing, and removing stop words to create a high-quality dataset is being preprocessed. The preprocessed data is then fed into the LSTM neural network, which is known for its ability to model long-term dependencies in sequential data. To enhance the sentiment analysis performance, a mutually inclusive classifier framework is proposed. Traditional sentiment analysis models often categorize sentiments into distinct classes, such as positive and negative. In contrast, our mutually inclusive classifiers allow for multiple sentiments to be assigned to a single tweet. This approach is better suited to capture the nuances and complexities of human emotions often expressed on social media platforms like Twitter. The LSTM model with mutually inclusive classifiers using Python and popular deep learning libraries like TensorFlow and Keras is implemented. The model is trained on a labeled dataset, and hyperparameters are tuned to optimize its performance. To evaluate the effectiveness of the approach it uses various metrics, such as accuracy, precision, recall, and F1-score. In the experimental analysis, demonstrated the LSTM-based sentiment analysis model with mutually inclusive classifiers outperforms traditional sentiment analysis models. The findings contribute to the advancement of sentiment analysis techniques and offer valuable applications in understanding and analyzing user sentiments in the dynamic world of social media. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Optimizing AUV Positioning and Data Transmission with Actor Critic (A3C) Algorithm in Underwater Wireless Sensor Networks 4

Optimizing AUV Positioning and Data Transmission with Actor ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kodukulla, Aruna Gayatri Amanullah, M. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method. © 2024 IEEE.

关键词： Sensor nodes

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Incorporation of Deep learning (DL) Techniques to make Intelligent and Adaptable Wireless Communication Systems

Incorporation of Deep Learning (DL) Techniques to make Intel...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Tiwari, Anamika Kumar, Sandeep Rengarajan, A. Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The emergence of Deep learning (DL) was a response to the widespread availability of mobile data and the growing complexity of wireless networks. It is a subfield of machine learning that has been proposed in academic literature. This technology is regarded as a breakthrough in the management of mobile data traffic. Wireless systems generate a significant amount of varied data, which presents computational issues. In order to tackle this problem, a variety of optimization methodologies are being investigated. DL increases wireless communication by enhancing its intelligence and adaptability. This paper offers an overview of the DL platform and several methodologies employed to enable deep learning in wireless communications. Wireless networking involves various disciplines, including networking, routing, and scheduling, all of which incorporate DL. This text encompasses a comprehensive range of topics. The topic encompassed several optimization methodologies and the latest breakthroughs in this domain. © 2023 IEEE.

关键词： Deep learning Fog computing Mobile network Reinforcement learning Wireless network

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