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1st International Conference on Advances in Electrical, Electronics and computational Intelligence, ICAEECI 2023

作者： Anitha Gracy, J. Parthasarathy, S. Bagavathilaksmi, R. Thiagarajar College of Engineering Applied Mathametics and Computational Science Madurai India VELS Institute of Science Technology and Advanced Studies Department of Computer Application/Information Technology Chennai India

ISBN: (纸本)9798350342796

This Software engineering (SE) is the core research area of the software industry, and numerous algorithms and frameworks are proposed every day. Software engineering is the discipline that keeps up with recent developments in fields and techniques such as data mining, machine learning, natural language processing, and artificial intelligence. It is difficult to manage such a massive amount of data in a software engineering repository if data processing and big data analytics are not available. This paper provides an in-depth study of data mining techniques that to improve the efficiency of the software development lifecycle. The study emphasizes the practical applications and discoveries made by text mining, clustering, classification, and other methods. © 2023 IEEE.

关键词： Classification Clustering Data Mining SDLC Software engineering Text Mining

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Prediction of Rental Demands using different Machine Learning Algorithms

Prediction of Rental Demands using different Machine Learnin...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, S. Vijay, K. Pavithra, S. Prithi, S. Kannan, Sriram Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineeirng Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Chennai India SRM IST School of Computing Department of Computational Intelligence Kattankulathur India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348217

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions. © 2023 IEEE.

关键词： Machine learning

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Malicious data detection in IoT using deep learning approach

Malicious data detection in IoT using deep learning approach

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Kolli, Srinivas Srinivasan, Aravindan Manikandan, R. Prasad, Shalini Kumar, Ashok Ramesh, S. Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication City Engineering College Karnataka Bangalore India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computational Intelligence Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

This article compares the efficacy of various DL intrusion detection techniques and identifies the optimal DL methodology for ID in the IoT. In this study, the DL techniques employed were artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent units (GRUs). The suggested model is assessed using a common dataset for IoT intrusion detection. Next, the experimental findings are scrutinized and contrasted with existing IoT techniques for intrusion detection. When compared to the previously used approaches, the suggested technique appeared to have the highest accuracy (99.9%). © 2024 The Author(s).

关键词： Intrusion detection

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Multimodal Pain Assessment Βased on Physiological Biosignals: The Impact of Demographic Factors on Perception and Sensitivity 11

Multimodal Pain Assessment Βased on Physiological Biosignal...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Pavlidou, Elisavet Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research & Technology-Hellas Vassilika Vouton Heraklion70013 Greece

ISBN: (纸本)9789897587436

Pain is a multidimensional and highly personalized sensation that affects individuals’ physical and emotional state. Visual analog scales, numeric rate indicators, and various questionnaires, all relying on patient-reported outcome measurements, are considered the "gold" standard methods for assessing the severity of pain. Nevertheless, self-report tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. The purpose of this study is to automate the process through multimodal physiological-data-driven machine-learning models in order to gain deeper insights into pain sensation. We developed a pipeline using electrocardiogram (ECG), galvanic skin response (GSR), and electromyogram (EMG), along with demographic information from the BioVid dataset. The Pan & Tompkins algorithm was applied for ECG signal processing, while statistical analysis was used for feature extraction across all signals. Our study achieved 82.83% accuracy in the SVM classification task of baseline (BL) vs the highest level of pain (PA4) for females aged 20-35. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Sensory perception

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Revolutionizing Cloud Computing: Evaluating the Influence of Blockchain and Consensus Algorithms 3

Revolutionizing Cloud Computing: Evaluating the Influence of...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Saravanan, T.R. Rathinam, Anantha Raman Lenin, J. Komathi, A. Bharathi, B. Murugan, S. Srm Institute of Science and Technology Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Kl University Department of Computer Science and Engineering Guntur Andhra Pradesh Vijayawada India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

Combining Proof of Space (PoSpace) and Proof of Replication (PoRep) with interoperability protocols and distributed file systems in cloud computing may change the game. This combination has the potential to usher in a paradigm change in cloud infrastructures, bringing with it new heights of safety, scalability, and efficacy. Both PoSpace and PoRep provide new ways of storing information on blockchain networks. The energy consumption of PoSpace is much lower than that of conventional Proof of Work (PoW) algorithms because it uses unused storage space as a resource for consensus. In contrast, PoRep strengthens the robustness of the cloud system by evaluating the storage replication between nodes to guarantee data integrity. Protocols for interoperability across blockchains are crucial to their capacity to communicate with one another. They make it easier to trade information and assets, leading to a more cohesive ecosystem that goes beyond the boundaries of different blockchains. Increased connectivity and productivity in the cloud are both benefits of interoperability. By distributing data storage over a network, distributed file systems usher in a new era of administration efficiency. © 2023 IEEE.

关键词： Cloud Computing Data Storage Distributed File Systems Efficiency Interoperability Protocols Proof of Replication (PoRep) Proof of Space (PoSpace) Resilience Scalability Security

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Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture 7

Deep Learning for Plant Disease Detection and Crop Yield Pre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Amudha, S. Bindu, G. Sankar, Sasi Rekha Poongothai, E. VijayaLakshmi, K. Sarveshwaran, Velliangiri Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai602203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350341485

Despite a sharp rise in population, agriculture still provides food for all people and acts as a backbone of the country. Nowadays, no proper guidance given to farmers and they are not able to analyze the nature of soil, disease of plant and its requirements. This leads to the depletion of farming in future. Crop diseases must be identified and prevented in order to increase the productivity. To overcome this, the nature of the soil is analyzed through various parameters like Nitrogen-Potassium-Phosphorus Content in soil, Humidity, Temperature, pH value of the soil, Rainfall using sensors, plants disease using cameras in real-time environment. These parameters are obtained using various IoT Sensors, cameras and existing Data, are analyzed through ML Algorithms. The proposed system consists of two modules in crop yield prediction system using the features NPP-WPF whereas NPP stands for Nitrogen-Potassium-Phosphorus and WPF stands for Water Content-Pesticide-Fertilizer, the Farmers can be guided with the details of the crop that is suitable for the soil and about Fertilizer, Pesticide, Water requirements. The second module called as automatic disease detection system uses CNN, ResNet, VGGNet, and Mobile net models to recognize and diagnose plant diseases from their leaves at the early stage. The proposed model is implemented with the dataset of 14 different species of plants and with the categorical classes of 38 classes. The result analysis shows that the suggested model provides accuracy of 98.2% when compared to the existing state-of-the-art methodologies. © 2023 IEEE.

关键词： Phosphorus

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Correction: ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification

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International Journal of computational Intelligence Systems 2025年第1期18卷 1-2页

作者： Lalithadevi, B. Krishnaveni, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

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Improving Quantum Circuit Synthesis with Machine Learning 4

Improving Quantum Circuit Synthesis with Machine Learning

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin University of California Department of Electrical Engineering and Computer Science Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9798350343236

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that mini-mize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7 x in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023 IEEE.

关键词： Machine learning

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Hourly Journal: A Mobile App for Optimizing Your Daily Routines 7

Hourly Journal: A Mobile App for Optimizing Your Daily Routi...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sureshkumar, S. Suryavarshini, S. Chellammal, P. Murugan, K R. Senthil Rani, P. Kavitha Kaviyaraj, R. J.J College of Engineering and Technology CSE Trichy India Sri Krishna College of Engineering and Technology CSE Coimbatore India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350397253

There are certain apps to record how people spent their day and whether they lived the day as per their goals. But these only make people feel guilty for having wasted the day or happy on being productive, but no app shows what is the pattern for success and failure. But with Hourly Journal it is possible to understand the pattern of daily time investments and fix it, empower it and grow in life overall. Mobile application development is becoming more popular due to the expanding popularity of smartphones and tablets. Traditional software development has had a significant influence on the development of mobile apps. However, the ultimate product is software designed to take use of mobile devices' specific capabilities and technology. To solve the problem of performance on each specific device, you must first design an app for that device. This implies writing code that is tailored to the exact hardware of a certain device. In the case of iOS devices, this is simple since mobile developers simply need to create apps for the iPhone and iPad to make their apps universally usable. For Android devices, on the other hand, the hardware and operating system versions vary from device to device. With the Hourly Journal available in the palm of your hand, people can use it as a modern-day diary, with revolutionary futuristic insights into the patterns of people's activities and tendencies. Based on tag tracking there is scope for human behavioral prediction in future too. © 2023 IEEE.

关键词： Software design

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Know-Thy-Basis: Decomposing F26 for Lightweight S-box Implementation

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2024年第4期2024卷 763-794页

作者： Sau, Dilip Sarkar, Sumanta Saha, Dhiman Mandal, Kalikinkar Center for Computational & Data Sciences Indian Institute of Technology Kharagpur India University of Warwick Coventry United Kingdom de.ci.phe.red Lab Department of Computer Science and Engineering Indian Institute of Technology Bhilai India University of New Brunswick FrederictonNB Canada

A recent trend has shown constructions of 6-bit S-boxes that are mostly focused on their cryptographic elegance, while their lightweight aspects have not really been addressed well. This paper attempts to plug-in this existing research gap where we show how the composite structure of the extension field F26 could be leveraged. An earlier well-known example is an efficient implementation of AES S-box using the tower field extension of F28. The case of F2ab is completely different from any tower field as the implementation varies as per the choice of extension – for instance, F(2a)b or F(2b)a, where a and b are prime. Thus, it makes the implementation of S-boxes over F26 = F2(2×3) very interesting. In this work, we systematically study the composite field structure of F26 from a hardware standpoint for a class of S-boxes that are power mapping or their affine equivalents. We analyze the hardware efficiency with respect to different representations of the field extension, i.e., F(22)3or F(23)2. Furthermore, for each extension, we investigate the impact of various choices of bases – for instance, we present the evidence of the effect that normal or polynomial bases have on the implementation. This gives us further insight on the choice of basis with respect to the field extension. In the process, we present a special normal basis, when used in conjunction with F(23)2 results in the least (or very close to least) area in terms of GE for the 18 (6 quadratic and 12 cubic) S-boxes studied in this work. The special normal basis reported here has some algebraic properties which make it inherently hardware friendly and allow us to predict the area reduction, without running a tool. Overall, this work constitutes an extensive hardware characterization of a class of cryptographically significant 6-bit S-boxes giving us interesting insights into the systematic lightweight implementation of S-boxes without relying on an automated tool. © 2024, Ruhr-University of Bochum. All rights

关键词： Cryptography

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