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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Ramesh R Veeramalai Sankaradass Nishintha A HarshivKrishna M R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The medicinal plants are particularly important in traditional health care systems, yet the right identification of medicinal plants is one of the great challenges because of diversity and similarity among species. In this paper, we introduce a new approach to herbal medicine classification using Vision Transformers, or simply ViTs. This deep learning architecture is at the front edge of approaching complex patterns within plant images through leveraging self-attention mechanisms. Unlike the classical CNN, Vision Transformers are significantly better than their counterparts on tasks related to classification as they concentrate on global relationships between image patches. We apply and test our model on a comprehensive dataset of medicinal plants. It is demonstrated that Vision Transformer works better in accuracy and generalization than existing CNN-based approaches. In addition, the model demonstrates robustness under different environmental conditions related to lighting, variations of backgrounds, and also other probable challenges in identification with the real world. This study also brings out the possibility of directly integrating the classification system based on Vision Transformers into applications that may be used in mobile devices, which will now enable real-time identification in fieldwork settings for herbal medicines. Our findings show that the proposed Vision Transformers push medicinal plant classification in new directions in traditional and modern medical settings.

关键词： Medicinal plants computer vision Accuracy Biological system modeling Lighting Medical services Transformers Robustness Real-time systems Biomedical imaging

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Smart Oral Health Monitor

Smart Oral Health Monitor

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Karthik S Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder.

关键词： Deep learning Image processing Teeth Cameras Real-time systems Mobile applications Monitoring Diseases

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Developing an Online Platform for Multimodal Lexical Learning for the Bengali Learners 3

Developing an Online Platform for Multimodal Lexical Learnin...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dash, Niladri Sekhar Deb, Srija Dutta, Nirnoy Majumder, Meghna Bhattacharyya, Amrita Mondal, Subhadip Pal, Alok Ranjan Linguistic Research Unit Indian Statistical Institute Kolkata India College of Engg. and Mgmt. Kolaghat Dept. of Computer Science and Engineering Kolaghat India

ISBN: (纸本)9781665494007

The usability of a lexical database of a natural language on an online platform is in great demand in this new era of online language education. An interface of this kind can primarily serve three different purposes: first, it plays an operational role in new frame of multimodal education system;second, it partly fulfills the necessity of online lexical resources which are in increasing need in research and application in the field of language technology;and third, it becomes a useful and omnipresent resource for native and foreign language users who want to learn meanings and usages of words of a natural language for different personal, academic, philanthropic and commercial purposes. Keeping these applications in view, in this paper, we describe the process that we apply to develop an online platform for the Bengali WordNet, which offers a unique opportunity for multimodal lexical learning to the Bengali learners. The proposed work, that we want to present in this paper, has the potentials to fulfill all the three needs stated above. While the WordNet provides different kinds of information directly associated with words, the digital platform has a very sensitive user-friendly interface for the access of the lexical resources stored in the WordNet. © 2023 IEEE.

关键词： Ontology

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Stop Reinventing the Wheel! Promoting Community software in Computing Education 22

Stop Reinventing the Wheel! Promoting Community Software in ...

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27th Annual Conference on Innovation and Technology in computer science Education, ITiCSE-WGR 2022

作者： Blanchard, Jeremiah Hott, John R. Berry, Vincent Carroll, Rebecca Edmison, Bob Glassey, Richard Karnalim, Oscar Plancher, Brian Russell, Seán Dept. of Engineering Education University of Florida GainesvilleFL United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States Polytech Engineering School LIRMM-Univ Montpellier CNRS Montpellier France Dept. of Computer Science Full Sail University Winter ParkFL United States Dept. of Computer Science Virginia Tech BlacksburgVA United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Information and Physical Sciences University of Newcastle CallaghanNSW Australia Dept. of Computer Science Barnard College Columbia University New York CityNY United States School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798400700101

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the Computing Education community. We hypothesise that this is due in part to discoverability, availability, and adaptability challenges. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to rely on community developed solutions that often lack a centralised authority and may be community or individually maintained. To this end, our working group explored what solutions are currently available, what instructors needed, and the reasons behind the above-mentioned phenomenon. To do so, we reviewed existing literature and surveyed the community to identify the tools that have been developed by the community;the solutions that are currently available and in use by instructors;what features are needed moving forward for classroom and research use;what support for extensions is needed to support further Computing Education research;and what institutional challenges instructors and researchers are currently facing or have faced in using community software solutions. Finally, the working group identified factors that limited adoption of solutions. This work proposes ways to integrate and improve the availability, discoverability, and dissemination of existing community projects, as well as ways to manage and overcome institutional challenges. © 2022 Owner/Author.

关键词： Open source software

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Enhanced Security Through Distributed Fragmentation And Encryption For Cloud-Based Data Storage

Enhanced Security Through Distributed Fragmentation And Encr...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Kaushik J Jensen Jose C R. Vasanthi Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This paper proposes a novel cloud data security paradigm for sensitive applications, integrating Equipartition File Segmentation (EFS), a hybrid encryption scheme, and distributed storage. EFS partitions files into uniform fragments, subsequently encrypted using alternating AES and Blowfish algorithms in Cipher Block Chaining (CBC) mode with unique Initialization Vectors (IVs). To ensure secure key management, an RSA key pair is generated; the AES/Blowfish keys are encrypted with the RSA public key and persisted within Firebase Firestore. Notably, the private RSA key is encrypted using a user-supplied password and stored within the Firebase Database alongside the public key. Encrypted file fragments are distributed across Amazon S3 and Azure Blob Storage to enhance data availability and resilience. File reconstruction is performed by retrieving the RSA-encrypted symmetric keys and the password-encrypted private RSA key, decrypting the private key, and subsequently decrypting and reassembling the file fragments through Sequential Data Reassembly. This architecture achieves enhanced data security through robust cryptographic techniques, secure key management protocols, and distributed storage, while maintaining acceptable performance characteristics.

关键词： Cloud computing Accuracy Distributed databases Public key Vectors Encryption Cryptography Reliability Standards Resilience

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Multiclass Brain Tumor Detection using Deep Transfer Learning

Multiclass Brain Tumor Detection using Deep Transfer Learnin...

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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Aishwarya V. Deshmukh M.R. Bendre Dept. of Data Science Engineering Prawara Rural Engineering College Loni India Dept. of Computer Engineering Prawara Rural Engineering College Loni India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

Brain tumors, identified as abnormal growth of cell inside the brain, necessitate accurate categorization for tailored treatment plans. This classification aids in determining the severity of the condition and guiding therapeutic decisions. Iimaging technologies in medical like Magnetic Resonance Imaging (MRI) are instrumental in identifying brain pathologies without emitting ionizing radiation. Deep learning, a subset of AI, enhances brain tumor detection from MRI scans, improving prediction rates. Digital image processing is vital for medical image analysis and diagnosis. Brain tumors manifest in various forms, categorized as benign or malignant based on their tissue characteristics. Convolutional Neural Networks (CNNs), particularly VGG-16, Inception-v3, and ResNet-50 are prominent in brain tumor analysis, segmentation, and classification. These models are compared for their efficacy in identifying and forecasting brain tumor cells. Common brain tumor types include glioma, meningioma, and pituitary tumors, each requiring specific treatment approaches.

关键词： Image segmentation Accuracy Image analysis Magnetic resonance imaging Transfer learning Feature extraction Real-time systems

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Leveraging compiler optimization for code clone detection 33

Leveraging compiler optimization for code clone detection

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33rd International Conference on software engineering and Knowledge engineering, SEKE 2021

作者： Singh, Shirish Singhal, Harshit Mishra, Bharavi Dept. of Computer Science Columbia University United States Dept. of Computer Science and Engineering The LNMIIT India

ISBN: (纸本)1891706527

Finding similar code in software systems can guide several software engineering tasks such as code maintenance, program understanding, and code reuse. Similar code detection has been actively studied in the past. In the paper, we propose a novel approach that leverages compiler optimizations to transform semantically similar code and detect similar programs. The key observation of our work is that the compiler optimizations can be used to smooth out source code level idiosyncrasies introduced by the developers, thus making the optimized programs, for the same task, similar in structure. The similarity in structure can then be used to classify the programs. We conducted experiments on the Google CodeJam dataset to demonstrate the effectiveness of our approach. The experimental results show that our technique can achieve up to 85% accuracy on the program classification task, which is an improvement of more than 25% over the source code level classification. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： computer software reusability

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Duality between Lines and Points

arXiv

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arXiv 2025年

作者： Saxena, Sanjeev Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur208 016 India

There are several notions of duality between lines and points. In this note, it is shown that all these can be studied in a unified way. Most interesting properties are independent of specific choices. It is also shown that either dual mapping can be its own inverse or it can preserve relative order (but not both). Generalisation to higher dimensions is also discussed. An elementary and very intuitive treatment of relationship between arrangements in d + 1 dimensions and searching for k-nearest neighbour in d-dimensions is also given. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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A 10-Bit Successive Approximation Register Analog to Digital Converter Using VCO-Based Time-Domain Comparator

A 10-Bit Successive Approximation Register Analog to Digital...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ko-Chi Kuo Jia-Wei Chen Dept. Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

The successive approximation register (SAR) analog-to-digital converter (ADC) architecture is employed to achieve low power consumption, allowing it to operate at low supply voltage. A time-domain quantization architecture based on a time-domain comparator is combined to overcome the design bottlenecks caused by the reduced intrinsic gain of transistors, the increased process variation, and the decreased supply voltage in advanced processes. A voltage-to-time converter based on a voltage-controlled oscillator (VCO) is used, providing the benefits of circuit stability and low noise impact. This design can be further improved by adding an SR time amplifier to the output of VCO, allowing the phase difference to grow exponentially. Hence, a faster escape from the dead zone of the phase detector can be realized to resolve the issue between high resolution and high speed in traditional VCO comparators. To accommodate the output generated by the improved voltage-to-time converter, the operation mode of the phase detector is modified, enabling it to be applied to the new voltage-to-time converter output. This paper presents a 10-bit SAR ADC in TSMC 40nm process, with a conversion rate of 10MS/s, a supply voltage of 0.9V, power consumption of 57.9µW, an ENOB of 9.88 bits, an SFDR of 74.07dB, and a FoM of 6.14 fJ/conv.-step.

关键词： Quantization (signal) Power demand Voltage-controlled oscillators Noise Detectors Voltage Registers Circuit stability Time-domain analysis Analog-digital conversion

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LipCraft: Building a Visual Speech Recognition System with Machine Learning and Streamlit

SSRN

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SSRN 2024年

作者： Karthikeyan, S. Sompalyam, Saivarshi Raveendran, Lekshmi S. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

In the tapestry of human communication, speech has long been regarded as a cornerstone, seamlessly weaving the fabric of understanding and connection. Yet, for individuals facing hearing impairments or navigating environments where audible communication is limited, the reliance on traditional speech recognition technologies proves restrictive. Enter the realm of Visual Speech Recognition (VSR), an innovative domain seeking to decipher spoken language by interpreting the nuanced movements of the lips. In the crucible of this cutting-edge pursuit, our research takes centre stage with the project "LipCraft: Building a Visual Speech Recognition System with Machine Learning.". This project," LipCraft: Building a VSR System with Machine Learning and Streamlit Integration," stands at the convergence of these technologies, offering a holistic approach to redefine the boundaries of communication. The genesis of LipCraft is fuelled by a profound motivation rooted in inclusivity and technological innovation. Recognizing the constraints of traditional speech recognition, particularly for those with hearing impairments, the project aims to transcend these limitations by harnessing the power of deep learning, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and the unique capabilities of a Connectionist Temporal Classification (CTC) decoder. The scope of LipCraft extends beyond theoretical exploration into the practical realm, encompassing applications in accessibility services, education, and content creation. The project's innovative integration of technologies is not confined to the realms of machine learning and deep learning alone. LipCraft also introduces a user-friendly application interface developed with the Streamlit framework, demonstrating a commitment to making the technology accessible and user centric. LipCraft's objectives are ambitious and multifaceted. At its core objective to elevate the precision and efficiency of visual speech rec

关键词： Long short-term memory

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