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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Vaishnavi, Sirikonda Shetty, Sanne Hruthika Maheswari, M. Jany Shabu, J.L. Refonaa, J. Sathyabama Institute of Science and Technology Department of Cse Chennai India Sathyabama Institute of Science and Technology Dept.of Computer Science and Engineering India

ISBN: (纸本)9798350375190

In recent years, the integration of Artificial Intelligence (AI) in healthcare has attracted a lot of attention, particularly in the domain of personalized nutrition. With the rising prevalence of diet-related health issues such as obesity, diabetes, and cardiovascular diseases, the primary objective of this research study is to design and implement a platform capable of analyzing individual preferences, nutritional needs, health goals, and lifestyle factors to offer customized dietary guidance and management. This research study proposes a AI Dietician by utilizing AI algorithms to provide personalized dietary recommendations. This study also covers the technical aspects and methods used in creating a AI dietician, including feature extraction, algorithm selection, data gathering strategies, and model training by ultimately contributing to the prevention of diet-related health issues and promoting long-term health outcomes. © 2024 IEEE.

关键词： Nutrition

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Navigating the Blockchain Landscape with a Comprehensive Survey of Smart Contracts, Ethereum, and Consensus Mechanisms 4

Navigating the Blockchain Landscape with a Comprehensive Sur...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Suresh Kumar, N. Dolly Brinthakumari, S. Partheeban, N. School of Computer Science and Engineering Ramanagara India Jagan Institute of Management Studies Department of Information Technology Delhi India New Horizon Institute of Technology and Management Department of Computer Engineering Thane India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350393354

In the dynamic landscape of blockchain technology, this research paper meticulously explores and contrasts two pivotal paradigmsSmart Contracts and Traditional Contractswithin the overarching framework of decentralized agreements. In the pursuit of this, the research diligently examines the subtle differentiations inherent in the various aspects of execution, intermediaries, trust, transparency, speed, costs, flexibility, security, and enforceability. While Smart Contracts, executed automatically through code on blockchain platforms such as Ethereum, offer unprecedented efficiency and transparency, Traditional Contracts, reliant on manual execution and potential intermediaries like lawyers, maintain a familiar yet potentially more cumbersome approach. The research extends its focus to Ethereum, a groundbreaking blockchain platform, spotlighting its instrumental role in upholding data security and integrity. The subsequent challenge under consideration is Ethereum's key aspects, such as decentralization, immutability, cryptography, smart contracts, consensus mechanisms, transparent ledger, tamper resistance, encryption, and permissioned access. A pivotal facet of this study centers on the comprehensive examination and comparison of consensus mechanisms employed in blockchain systems. This research assesses and compares key parameters of prominent blockchain consensus mechanisms, emphasizing factors like accuracy, time efficiency, scalability, and energy efficiency. The research culminates in a robust conclusion that synthesizes key findings, emphasizing Ethereum's role in ensuring data security and integrity, and the intricate landscape of consensus mechanisms. © 2024 IEEE.

关键词： Smart contract

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Paddy disease classification using machine learning technique

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Materials Today: Proceedings 2022年 64卷 883-887页

作者： Sobiyaa, P. Jayareka, K.S. Maheshkumar, K. Naveena, S. Rao, Koppula Srinivas Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu Erode India Department of Computer Science & Engineering Sona College of Technology Erode Erode India Department of Computer Engineering Sri Ranganathar Institute of Polytechnic College Athipalayam Tamil Nadu Coimbatore India Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Hyderabad India

Early disease detection plays a vital role in protection of paddy crops. In earlier days the detection of disease was done through seeing or by examining in a laboratory. The observation made visually needs experts and it might vary for each individual which leads to error and laboratory testing requires more time and might not be able to deliver the outcome within a time. To get the better of this issue, image processing-based Machine learning approach used to detect the diseases and classify the diseases. We mainly focused on rice (Oryza sativa) diseases. The images contain the leaves and stems which are affected by disease collected from the paddy fields. The dataset contains five different classes of diseases (1) Rice Blast (2) Bacterial Leaf Blight (3) Sheath Blight (4) Healthy leaves. The early detection of diseases will help farmers to increase their yield. © 2022

关键词： Machine learning

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A Study of the Impact of Implementing a Procedure for Creation of Risk Factor Software 1

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8th International Conference on computers, Management and Mathematical Sciences, ICCM 2022

作者： Sriramulu, Mahanti Patro, Satyabrata Joycee, G. Nirmala Ashok, Kondapalli Department of Computer Science and Engineering Dhanekula Institute of Engineering & Technology Andhra Pradesh Vijayawada India Visakhapatnam India Department of MCA St Ann’s college for Women Telangana Hyderabad India Department of Computer Science and Engineering Bonam Venkata Chalamayya Institute of Technology and Science Batlapalem India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

When applied to the worldwide advancement of programming, the ob- stacles of requirements engineering become doable. Something is difficult for a variety of reasons. Chances are you’re one among them because the global im- provement outlook is more receptive to gambling. As a result, it could be one of the primary reasons for taking requirement engineering testing seriously. To begin, it is vital to identify the variables that actually cause these dangers. The factors and threats that these elements may bring about are then separated in this chapter. An orderly writing survey is done in the framework of the worldwide programming improvement perspective for the observable evidence of these variables and the hazards that may emerge during the necessity designing cycle. The list suggests a moderate improvement for aiding exercises in necessity designing in a global programming advancement worldview. This work is very beneficial for those with less experience working in global programming advancement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Global optimization

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Complexity of Monomial Prediction in Cryptography and Machine Learning 26

Complexity of Monomial Prediction in Cryptography and Machin...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Dutta, Pranjal Rajasree, Mahesh Sreekumar Sarkar, Santanu Singapore Indian Institute of Technology Delhi Department of Computer Science and Engineering Delhi India Indian Institute of Technology Madras Department of Mathematics Tamil Nadu India

ISBN: (纸本)9798331532833

In this paper, we focus on the monomial prediction problem in two settings: (1) Decide whether a particular mono-mial m is present in a composite function f:=fr° fr-1° ldots fo, where fi are quadratic boolean functions, (2) Decide whether a particular monomial m is present in a composite function f:= fr° fr-1° ldots f0, where polynomials fi are efficiently computable by Probabilistic Generating circuits over rationals. Probabilistic generating circuits (PGCs) are economical representations of multivariate probability generating polynomials (PGPs), which capture many tractable probabilistic models in machine learning. The first problem has a strong connection with the security of symmetric-key primitives. Dinur and Shamir proposed the cube attack for distinguishing a cryptographic primitive from a random function, which can be thought of as an efficient monomial prediction. In a general setting, over any large finite field or integers, monomial prediction is known to be NP-hard. Here, we show that in the quadratic setting, the problem is oplus P complete. oplus P is an interesting complexity class that is not known to contain NP, however, it is believed to contain computationally hard problems. On the other hand, we also present several new zero-sum distinguishers for 5-round Ascon, which is one of the ten finalists for NIST light weight cryptography standardization competition. We show that the second problem is #P-complete. It is known that PGCs have efficient inference, i.e. given a monomial, one can efficiently output (which signifies the probability) its coefficient in the polynomial computed by the circuit. However, a composition of such functions makes the inference hard. Composition of prob-abilistic models and their efficient inference play a crucial role in the semantic contextualization and framework of uncertainty theories in graphical modelling. © 2024 IEEE.

关键词： Boolean functions

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Enhancing Underwater Images with Color Correction and UW-Net technology 3

Enhancing Underwater Images with Color Correction and UW-Net...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chandrasekaran, Hariharasudhan Bhatt, Pooja Tiwari, Vinita Rastogi, Shailesh Chennai Institute of Technology Department of Civil Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Vadodara India Pune India Department of Finance Pune India

ISBN: (纸本)9798331508456

The profound importance of effective underwater image restoration is well-recognized across a variety of domains including underwater exploration, marine biology, environmental monitoring, and autonomous underwater vehicle operations. Current methodologies, however, struggle to adequately address the inherent challenges of underwater imaging, such as severe color distortion and poor visibility due to light absorption and scattering. This paper introduces UW-Net, an innovative end-to-end neural network model specifically tailored for underwater image restoration. UW-Net ingeniously integrates discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) to enhance the extraction and reconstruction of underwater image features. A pivotal aspect of UW-Net is its novel color correction strategy which specifically targets the red and blue spectral distortions, typical of underwater imaging. Additionally, the network incorporates a U-Net architecture with DWT for effective down-sampling and IDWT for precise up-sampling, alongside a chromatic adaptation transform layer aimed at boosting image contrast and color fidelity. Extensive training and evaluation on prominent datasets reveal that UW-Net significantly outperforms existing methods, offering substantial improvements in various performance metrics. This paper delineates the design, implementation, and testing of UW-Net, showcasing its superior capability to restore high-quality visuals from degraded underwater images. © 2025 IEEE.

关键词： Underwater photography

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Swa Bhasha 2.0: Addressing Ambiguities in Romanized Sinhala to Native Sinhala Transliteration Using Neural Machine Translation 4

Swa Bhasha 2.0: Addressing Ambiguities in Romanized Sinhala ...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Dharmasiri, Sachithya Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology Colombo06 Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

With the growing popularity of social media and instantaneous messaging, it is more important than ever to interact online in your native language. In Sinhala, both Romanized and native Sinhala are widely used. Due to the informal textual abbreviation known as 'Singlish' however, attempts to translate Romanized Sinhala into native Sinhala via machine transliteration may result in errors. Rule-based transliteration systems may not be compatible with the ad hoc transliterations used in Singlish. To translate Romanized Sinhala back precisely and consistently into Native Sinhala, a novel NMT approach has been proposed. To address the complexities of casual Romanized Sinhala, a hybrid strategy combining rule-based and neural machine translation has been proposed. This strategy aims to eliminate word selection ambiguity by selecting the best word suggestions from a pool of predicted words using a suggestion algorithm. Combining the advantages of Suggestion algorithms and neural machine translation, the proposed transliterator has the potential to considerably enhance reverse transliteration and improve communication in native Sinhala by combining the strengths of both approaches. After completing the GRU model, the performance of the machine translation models on the BLEU test improved to 0.8, indicating high word-level translation accuracy. Significant potential exists for the proposed transliterator to enhance reverse transliteration and improve communication in Sinhala. While preliminary test results are promising, additional testing and refinement are required to improve the overall efficacy of machine translation models. © 2024 IEEE.

关键词： Neural machine translation

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Detecting Uteropelvic Junction Obstruction at an Early Stage using Ultrasound Images through an Interplanetary File System 5

Detecting Uteropelvic Junction Obstruction at an Early Stage...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Kaviya, R. Prasad, Mridula Pavithra, S. Kapali, B. Suresh Chander Vellore Institute of Technology School of Computer Science and Engineering Chennai India Easwari Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350389609

Ureteropelvic junction obstruction (UPJO) is a medical condition characterized by a blockage at the junction between the ureter and the kidney, leading to impaired urine flow and increased fluid pressure. If left untreated, UPJO can progress to hydronephrosis and potentially result in irreversible kidney damage. This paper presents a novel system for the early detection and severity grading of UPJO using Deep Learning computer-Aided Diagnosis (DL-CAD) techniques. The proposed approach leverages ultrasound imaging as a primary diagnostic tool, noted for its cost-effectiveness, absence of radiation, and non-invasive nature. To enhance the security and integrity of medical data transmission, we integrate blockchain technology into our framework. Our solution combines a DL- CAD model featuring an attention-based pyramid semantic segmentation network with a discrete wavelet transformation- processed residual classification network. An IoT device is employed to detect patient abnormalities and transmit this data to the diagnosis model via an InterPlanetary File System (IPFS). This decentralized storage solution facilitates secure data management across a peer-to-peer network. Ultimately, the proposed framework not only identifies the presence of UPJO but also assesses its severity, ensuring that patient data remains protected throughout the diagnostic process. © 2024 IEEE.

关键词： Semantic Segmentation

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AI-Driven Digital Mirror technology for Securing IoT-Enabled Smart Infrastructures 2

AI-Driven Digital Mirror Technology for Securing IoT-Enabled...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Chaudhary, Jyoti Jain, Upma Sudarshan, N. Pai, Vasudeva Sutaria, Kamal Natrayan, L. Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering NMAM Institute of Technology NITTE Deemed to be University Karkala India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Mechanical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331504960

The expansion of the Internet of Things (IoT) is driven by the proliferation of interconnected wireless gadgets, made possible by ever-improving computer and Internet infrastructure. The IoT is an extensive system of interconnected sensors and gateways. In the course of their constant environmental interaction, IoT devices produce a wealth of data resources, including images, audio, video, digital signals, and more. Connectivity to the Internet allows all Internet of Things (IoT) equipment and apps to exchange data and resources more effectively. The proposed AI-Driven Digital Mirror technology effectively enhances the security of IoT-enabled smart infrastructures by integrating real-time monitoring, anomaly detection, and adaptive response mechanisms. By employing advanced AI algorithms such as Graph Neural Networks (GNNs) and Reinforcement Learning (RL), the technology mirrors IoT environments digitally, enabling dynamic modeling and threat assessment. The system demonstrated a significant improvement in threat detection accuracy, achieving a precision rate of 95.6% and a recall rate of 93.2% during simulations. Additionally, the adaptive response mechanism reduced the average response time to security breaches by 40%, ensuring minimal disruption to infrastructure operations. Comparative analysis with traditional security solutions highlighted the system's superiority in mitigating complex, multi-layered attacks, and its scalability makes it suitable for various smart infrastructure applications, including smart cities, industrial automation, and intelligent transportation systems. The results underscore the potential of AI-Driven Digital Mirror technology in securing IoT ecosystems against evolving cyber threats while maintaining operational efficiency and resilience. © 2024 IEEE.

关键词： Gateways (computer networks)

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Machine Learning Yield Forecast in the IoT System for Cultivating Accuracy 2nd

Machine Learning Yield Forecast in the IoT System for Cultiv...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Jayakarthik, R. Kumar, Santosh Goswami, Sohan Mahaveerakannan, R. Hafeez, Shaik Abdul Tamilnadu Chennai India Department of Computer Science ERA University Uttar Pradesh Lucknow India Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Panchrakhi India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9789819776023

Crop production is a very complicated attribute that depends on a number of variables, including genotype, environment, and their interactions. The functional relationship between yield and these interaction elements must be fundamentally understood for accurate yield prediction to be possible, and both large datasets and potent algorithms are needed to show this relationship. In order to accurately estimate crops, we applied cutting-edge machine learning techniques. Our ability to gather data for huge agricultural areas spread throughout far-flung regions of the globe is made possible by the Internet of Things’ architectural principles. So that we may make forecasts about crops, our machine learning system can use this data. Nitrogen, phosphorus, potassium, temperature, humidity, and rainfall all have a role in determining the best crop to grow. Recommendations are made in light of these factors. There are 2200 total occurrences in the data collection, and there are 8 associated attributes for each one. For each of the 8 possible characteristic combinations, around twenty-two distinct plant species are available as potential options. The most effective model can be produced by utilizing the supervised learning approach and few of the machine learning methods available in WEKA. As prospective possibilities for the methods of machine learning that would be used in the classification process, the decision table classifier and the multilayer perceptron rules-based classifier JRip were selected. The system’s design took into account both the growing Internet of Things and the essential measurements required for farming. The average weighted value of the Receiving Operator Attributes has been found to be 1, the performance measured by the classifiers that were chosen has a value of 98.2272%, and the maximum time needed to construct the model is 8.03 s. Employing machine learning in agriculture aims to increase the production and nutritional value of the plants generated

关键词： Adversarial machine learning

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