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3rd International Conference on Communication, Devices and Computing, ICCDC 2021

作者： Das, Bidyut Sekh, Arif Ahmed Majumder, Mukta Phadikar, Santanu Department of Information Technology Haldia Institute of Technology Haldia India School of Computer Science and Engineering XIM University Bhubaneswar India Department of Computer Science and Application University of North Bengal Darjeeling India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal India

ISBN: (纸本)9789811691539

Image caption or description generation is a fundamental problem of artificial intelligence. It requires both knowledge, natural language processing, and computer vision together. It automatically produces description(s) from an image. The Bengali language is the fifth language spoken widely in the world. It is considered in many areas of research and development. Various established datasets are available in the literature for image caption generation in English since no such standard dataset exists for Bengali. This paper proposes a model for automatic image captioning in the Bengali language. This study uses a Bengali dataset to train the encoder–decoder neural network model. The proposed model trained with two descriptions per image and achieved a 0.67 BLEU-1 score. We expect that a far better result is obtained with a larger and more diverse dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages using machine learning and Transformer models 14

Sentiment Analysis and Homophobia detection of YouTube comme...

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14th Forum for Information Retrieval Evaluation, FIRE 2022

作者： Varsha, Josephine Bharathi, B. Meenakshi, A. Department of CSE Sri Siva Subramaniya Nadar College of Engineering Tamil Nadu India Department of Computer Science and Application SRM Institute of Science and Technology Tamil Nadu India

Sentiment Analysis is the task of identifying the emotions underlying the subjective opinions or emotional responses pertaining to a given topic, be it positive, negative or neutral. Sentiment Analysis is done with the use of natural language processing. Homophobia speech is a type of hate speech directed towards LGBT+ people. This research work presents Sentiment Analysis and Homophobia detection in Youtube comments in Code-Mixed Dravidian Languages with different embeddings using machine learning algorithms. The goal of Task- A is to identify sentiment polarity of the code-mixed dataset of comments, posts in Tamil-English, Malayalam-English, and Kannada-English collected from social media. The goal of Task-B is to identify if the comment is homophobic/transphobic in nature. Our team srmnlp worked with code-mixed form of Tamil, Malayalam and Kannada text provided by the FIRE 2022 organizers. Pre-trained models such as bert, xlm, MPNet were used along with classifiers such as SVM,MLP, Random Forest under the feature extraction techniques like, Count Vectorizer, and TF-IDF. The rankings for sentiment analysis task are, rank 1 in Tamil dataset, rank 6 in Malayalam dataset, rank 7 in Kannada dataset. The highest F1-score of 0.63 was obtained for sentiment analysis in Malayalam dataset, similarly 0.95 was obtained for homophibia detection task in Malayalam dataset. The performance of the proposed system is compared with various machine learning algorithms. © 2022 Copyright for this paper by its authors.

关键词： Adaptive boosting

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Recognition and evaluation of cutaneous condition through assorted artificial intelligence reliant algorithms

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Mishra, Manmohan Yadav, Ajay Kumar Mazumdar, Bireshwar Dass Gupta, Prashant K. Panwar, Arvind Bharadwaj, Shivam Department of Computer Application United Institute of Management Prayagraj India School of Computer Science & Engineering Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Prayagraj 203201 India

Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life threatening diseases. The most prioritized step toward restoring health is early illness signs identification. Identifying Cutaneous Condition from clinical images is one of the foremost challenges in medical image investigation. In the presented study we will enlighten the various Artificial Intelligence techniques falling under the categories of supervised machine learning including Probabilistic classifier (Naïve Bayes), Statistical algorithm (Logistic Regression), Ensemble learning (Random Decision Trees), Data analysis technique (Convolutional Neural Network) and Kernel approach (Support Vector Machine) to identify and classify the cutaneous condition appropriately so that corrective measures of skin treatment can be endow with. The proposed approach entails collecting images as input, preprocessing, segmenting, feature extraction and lastly applying the classification algorithms to derive the Cutaneous Condition categories. Additional trials are conducted using the different approaches as indicated and it was discovered from the suggested tests that the Convolutional Neural Network strategy yields the best results overall. The proposed model is trained, tested, and evaluated using the International Skin Imaging Collaboration (ISIC) 2019 challenge dataset and Human Against Machine with 10,000 training images (HAM10000) for the detection of manifold Cutaneous Condition. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Artificial intelligence Confusion matrix Convolutional neural networks Cutaneous condition Image processing Machine learning

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Efficient Restoration for Binary Neural Networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2025年第2期53卷 568-580页

作者： Zeng, Kai Wan, Zi-Xin Wang, Ming-Tao Shen, Tao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Yunnan Kunming650500 China

Restoring the weight distribution, activation distribution, and gradient to the original full precision network data as much as possible can greatly improve the inference ability of the binary network. However, existing methods directly apply the restoration operation in forward propagation to binary data, and the gradient approximation functions for back-propagation are fixed or manually determined, resulting in the need for improvement in the restoration efficiency of binary networks. To address this problem, the efficient restoration method is investigated for binary neural networks. Firstly, a distribution recovery method for maximizing information entropy is proposed. By shifting the original full precision weight mean and scaling the modulus, the quantized binary weight directly has the characteristic of maximum distribution restoration. At the same time, a simple statistical translation and scaling factor is used to greatly improve the restoration efficiency of weight and activation. Furthermore, it is proposed a gradient function based on adaptive distribution approximation, which dynamically determines the update range of the current gradient in the P-percentile according to the actual distribution of the current full precision data. It adaptively changes the shape of the approximation function to efficiently update the gradient during the training process, thereby improving the convergence ability of the model. On the premise of ensuring the improvement of execution efficiency, theoretical analysis has confirmed that the method proposed in this paper can achieve maximum restoration of binary data. Compared with the existing advanced binary network models, the experimental results of our method show excellent performance, with a 60% and 67% reduction in computational time for the distribution restoration operation quantization of ResNet-18 and ResNet-20, respectively. An accuracy of 93.0% is achieved for VGG-Small binary quantization on the CIFAR-10 dataset

关键词： Restoration

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Role and application of Ribs in Solar Air Heater 6

Role and Application of Ribs in Solar Air Heater

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singh, Amandeep Joshi, Sanjeev Kumar Kumar, Dharmendra Kumar, Rajiv Pandey, Shiv Dayal Thapliyal, Madhur Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Uttaranchal School of Computing Sciences Uttaranchal University Uttarakhand Dehradun India Shivalik College of Engineering Computer Science and Engineering Department Uttarakhand Dehradun India Graphic Era Hill University Department of Computer Application Uttarakhand Dehradun India

ISBN: (纸本)9798350304480

Maintaining the clean environment and reducing the usage of fossil fuels requires switching to renewable sources of energy, because of limited resources of fossil fuels and having insufficient fossil fuel storage. The world is facing from shortage of fossil fuel and its cost is going too high and its smoke is very harmful to the environment. In this crisis, the world is starting the use of renewable energy. Renewable energies are easily available, and it reduces air pollution and fuel cost. non-conventional energy sources, like solar energy, atomic energy, hydroelectric power, wind energy etc. are a substitute for conventional energy. Solar energy is the best one, is used in various utilization, like solar air heaters, solar water heaters, solar power generation, solar pumps, solar cooking etc. Solar air heater that absorbs heat from the sun's radiations to heat air and it is collected by the collector & converted into kinetic energy and also restored in the form of potential energy. In this review, we will discuss the role of ribs in solar air heating systems to enhance the heat transfer rate, types of rib, and the purpose of ribs arrangement in solar air heating systems. Ribs are used to transfer maximum heat and increase the pressure inside of the cabin and it located on the absorber plate surface and it help to increase the solar air heaters performance. This study emphasizes exploring the effects of ribs and the behaviour of fluid after impacting on the ribs. For drying crops, heating buildings, and other uses, solar air heaters are extremely important in the agriculture sector, small-scale industries, in urban and villages for heating and drying purposes, etc. The use of ribs can enhance the effectiveness of a solar air heating system and this technology can be used for heat transmission enhancement, power generation & can fulfil the world's energy requirements. The primary objectives of this review article are to assist and summaries the research work carried

关键词： Energy Heat transfer Power generation Renewable energy Ribs Solar air heater Solar energy

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Data Augmentation and its application in Histopathological Oral Cell Image Classification 1

Data Augmentation and its Application in Histopathological O...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Das, Madhusmita Dash, Rasmita Siksha 'o' Anusandhan Deemed to Be University Department of Computer Application Odisha Bhubaneswar India Siksha '0' Anusandhan Deemed to Be University Department of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665454933

Deep learning and Deep Neural Networks (DNN) is one of the most popular fields of Machine Learning. The working principle of DNN and deep learning is based on the Artificial Neural Network (ANN). The fundamental of ANN is grounded on human brain, so as DNN also tries to mimic human brain. From various DNN structure, Convolutional Neural Network (CNN) is one of the leading DNN structure used in the area of image classification problems. Although the performance of CNN in image classification problem is outstanding as compare to other traditional machine learning algorithm, but CNN has certain limitation. One of the most challenging issues of CNN is, insufficient data set problem. To learn a CNN model, huge amount of training data set is required, but in real time scenario, huge amount of data set is not practically available. Data augmentation is one of the solutions for this insufficient data set problem in CNN. In this paper author has discuss various approaches of data augmentation technique. In data augmentation technique, new data set is generated from the existing one so as to increase the size of the data set. In this work to analyze the performance of data augmentation, author has proposed a 14 layered CNN model, and observe the role of data augmentation, by using the proposed model for the classification of histopathological oral cell image data set. The performance analysis of the proposed CNN model is also compared with the state-of-the-art model like VGG16 and VGG19 and the proposed model outperform with 90.01% of testing accuracy using augmented data set whereas, using the original data set the proposed model got testing accuracy of 80.07%. © 2022 IEEE.

关键词： Convolutional neural networks

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Forecasting of Cardiovascular Disease Using Machine Learning 3

Forecasting of Cardiovascular Disease Using Machine Learning

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Azarudeen, A. Hemavathy, P. Jabbar, Abeer Mohsen Abed, Sarah Jassim Fhadil, Fay Ali, Aqeel Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute of Technology and Science Department of It Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

heart disease or cardiovascular disease includes several circumstances that affect the heart and is one among major reason of putting to demise globally. Here, we employ Machine Learning to predict cardiovascular disease using a data set containing 14 components. computers are taught to learn knowledge on their own using machine learning, a form of artificial intelligence. Machine learning in healthcare is increasingly being used and helps victims and clinicians in a variety of ways. The automated identification and diagnosis system's performance is comparable to that of a skilled radiologist. This article presents multiple heart disease-related characteristics, and includes models constructed using supervised learning techniques such as the Random Forest and K-Nearest Neighbor algorithms. It also focuses on which patients, given certain medical characteristics, are more likely to suffer cardiovascular-disease. © 2023 IEEE.

关键词： Forecasting

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Blockchain based Security Solution for Internet of Drones 2

Blockchain based Security Solution for Internet of Drones

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Maurya, Sudhanshu Niveditha, V.R. Anandapriya, B. Verma, Rohan Mirza, Kashish Tiwari, Mohit School of Computing Graphic Era Hill University Bhimtal Campus Nainital India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Patrician College of Arts and Science Department of Computer Application Chennai India Hemvati Nandan Bahuguna Garhwal University Department of Computer Science and Engineering Uttarakhand Srinagar India Graphic Era Hill University Bhimtal Campus Department of Computer Science and Engineering Nainital India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665474160

Nowadays, Drone usage is increasing faster than before. They have frequently been employed in civil services. In a number of sectors, including transportation, agriculture, building, and emergency response, drones have grown in significance. Drones can operate in places that are inaccessible to humans, collect data more effectively than conventional techniques, and carry out tasks more quickly. With the development of technology and laws, their use is anticipated to increase in the future. Therefore, it is imperative that drones must become smarter, more private, and more secure as they offer data transfer secrecy and reliability. In this paper, we have proposed a blockchain-based security solution model for the Internet of Drone environment. The proposed method is evaluated on a limited number of pretend drones. Here we may easily authenticate the administrator, add new drones, authenticate a drone, delete drones, get data from drones, save it to a blockchain-based secure system, and securely retrieve that information. © 2022 IEEE.

关键词： Drones

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A new spaceborne/airborne bistatic synthetic aperture radar time synchronization method based on fractional Fourier transform 5

A new spaceborne/airborne bistatic synthetic aperture radar ...

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2023 5th International Conference on Information science, Electrical, and Automation engineering, ISEAE 2023

作者： Zhou, Fang Gui, Changchun Hu, Xinxiao Xing, Mengdao Zhang, Jiajia School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Electronic Engineering Xidian University Xi'an710071 China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei230088 China

ISBN: (纸本)9781510667440

With the change of observation demands, traditional monostatic synthetic aperture radar(SAR) can no longer meet the requirements such as passive reception and cooperative multimode imaging. Spaceborne/Airborne hybrid bistatic SAR(SA-BSAR) emerges as the times require, and has been widely concerned with its flexibility, concealment. However, as the receiver and transmitter do not share the same frequency oscillation source, the impact of time error on imaging cannot be ignored as the satellite operates for a long time. At the same time, SA-BSAR is in the form of double root sign in the course of slant range. How to obtain two-dimensional(2-D) spectrum is also the key to imaging. This paper proposes a new method to estimate time error based on fractional Fourier transform (FRFT), which simplifies the traditional time error estimation steps. Finally, the Air-Phase method is used to solve the 2-D spectrum to achieve the target imaging. The algorithms proposed in this paper are implemented by FFT without interpolation, which ensures the imaging efficiency. The final experimental results show the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation via Neural Networks

arXiv

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arXiv 2024年

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College West Bengal India Department of Computer Science and Engineering JIS College of Engineering West Bengal India

In the emergent realm of quantum computing, the Variational Quantum Eigensolver (VQE) stands out as a promising algorithm for solving complex quantum problems, especially in the noisy intermediate-scale quantum (NISQ) era. However, the ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach to ameliorate this challenge by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations. By employing the Qiskit framework, we crafted parameterized quantum circuits using the RY-RZ ansatz and examined their behavior under varying levels of depolarizing noise. Our investigations spanned from determining the expectation values of a Hamiltonian, defined as a tensor product of Z operators, under different noise intensities to extracting the ground state energy. To bridge the observed outcomes under noise with the ideal noise-free scenario, we trained a Feed Forward Neural Network on the error probabilities and their associated expectation values. Remarkably, our model proficiently predicted the VQE outcome under hypothetical noise-free conditions. By juxtaposing the simulation results with real quantum device executions, we unveiled the discrepancies induced by noise and showcased the efficacy of our neural network-based ZNE technique in rectifying them. This integrative approach not only paves the way for enhanced accuracy in VQE computations on NISQ devices but also underlines the immense potential of hybrid quantum-classical paradigms in circumventing the challenges posed by quantum noise. Through this research, we envision a future where quantum algorithms can be reliably executed on noisy devices, bringing us one step closer to realizing the full potential of quantum computing. © 2024, CC BY.

关键词： Extrapolation

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