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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Priya, L. Kiruthika, A. Kumar, P. Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819788606

The agriculture industry is fundamental to the foundation of a country and is essential to the promotion of economic prosperity. It is crucial to ensure that Monitoring the health and detecting leaf infections in plants is crucial for maintaining sustainable agriculture. The actions taken to identify leaf infection represent a significant challenge in the agricultural industry. Manually recognizing plant diseases by analysing leaf images can be a time-consuming undertaking. In response to this challenge, continuous advancements in technology, particularly in image detection, have become instrumental. These advancements enable the accurate and prompt recognition of leaf infections and pests, facilitating the development of early treatment strategies. The paper objective is to precisely recognize diseases in leaf images. The essential steps in this procedure involve pre-processing, training, and recognition. Artificial Intelligence emerges as a ground breaking innovation, empowering the training of machines with image datasets to autonomously identify diseases in crops. Using this proposed methodology this system aims to improve the speed, accuracy, and overall effectiveness in identifying and managing crop infections across diverse agricultural settings through the utilization of machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agricultural economics

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Comparative Analysis of Cyberbullying Detection: A case study for Turkish and English

Comparative Analysis of Cyberbullying Detection: A case stud...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Najib, Ali Ozel, Selma Ayse Coban, Onder Cukurova University Department of Computer Engineering Adana Turkey Ataturk University Department of Computer Engineering Erzurum Turkey

ISBN: (纸本)9798350306590

In this study, we have implemented a significant number of experiments using machine learning (ML), deep learning (DL), and feature extraction techniques to detect cyberbullying in both Turkish and English languages. The results showed that our model performed well using DL with FastText word embedding on both English and Turkish datasets. However, the pre-trained variants of BERT (a language model) performed exceptionally well, achieving an average F-score of 0.9775 for the Turkish dataset and 0.9892 for the English dataset. These findings highlight the effectiveness of using advanced natural language processing techniques for cyberbullying detection in different languages. © 2023 IEEE.

关键词： Feature extraction

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Applications of Polymer Nanocomposites in High Voltage engineering: A Concise Review on Possible Applications and Questions Regarding Some Limitations

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Journal of engineering Science and Technology Review 2023年第5期16卷 156-163页

作者： Tsianakas, Vassilios Danikas, Michael G. Sarathi, Ramanujam Falekas, Georgios Democritus University of Thrace School of Engineering Department of Electrical and Computer Engineering Xanthi67100 Greece Indian Institute of Technology Madras Department of Electrical Engineering Chennai India

Polymer nanocomposites have been a topic of intensive research regarding High Voltage engineering since the nineties of the last century. They present an alternative to conventional polymers since the latter were diagnosed with certain drawbacks. In this paper, a short review is given as to the basic characteristics of the polymer nanocomposites and some comments are offered as to the possible applications of the said materials in High Voltage engineering. © 2023 School of Science, IHU. All rights reserved

关键词： Partial discharges

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A Multi-modal Approach for Emotion Recognition Through the Quadrants of Valence–Arousal Plane

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SN computer Science 2023年第5期4卷 1-12页

作者： Dutta, Stobak Mishra, Brojo Kishore Mitra, Anirban Chakraborty, Amartya Department of Computer Science and Engineering GIET University Odisha Gunupur 765022 India Department of Computer Science and Engineering Amity University West Bengal Kolkata 700135 India Department of Computer Science and Engineering University of Engineering and Management West Bengal Kolkata 700160 India

Emotion recognition has become a popular area of research in recent years, as emotions play a significant role in our social lives. There are many internal and external factors that trigger emotional changes, making it important to develop effective methods for identifying and managing emotional states. One such method is the use of physiological signals, which has revolutionized the treatment and diagnosis of mental health conditions. In this work, the authors focus on using electroencephalogram (EEG) and electrocardiogram (ECG) signals collected from a standard dataset to classify four emotional states based on the Arousal–Valence plane. The states considered are High Arousal and Low Valence (HALV), Low Arousal and Low Valence (LALV), High Arousal and High Valence (HAHV), and Low Arousal and High Valence (LAHV). The signals were collected non-invasively using sensors, and different standard machine intelligence algorithms were used to classify the emotional states. The experiments were conducted in two phases, and the results showed that the k-Nearest Neighbors algorithm was effective in handling class-imbalanced data, while the Logistic Regression algorithm outperformed other algorithms with an F1 score of 53.5% when trained with class-balanced data. This study presents a novel approach to emotion recognition and provides important insights into the use of physiological signals for identifying emotional states. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ECG EEG Emotion detection Machine learning Valence–Arousal

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Effectiveness of Almost Sub Frame Approach as Inter Cell Interference Management in LTE-A Macro - Pico Scenario 9

Effectiveness of Almost Sub Frame Approach as Inter Cell Int...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Jember, Kehali A. Abose, Tadele A. Kassahun, Ermias Madhav, T. Venu Adama Science And Technology University Department Electronics And Communication Engineering Adama Ethiopia Mettu University Department Electrical And Computer Engineering Ethiopia Gondar University Department Electrical And Computer Engineering Gondar Ethiopia

ISBN: (纸本)9798350394474

Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to offload data from the congested macro cells towards the small cells. However, cross tier interference is a serious issue, which is introduced due to the transmitted power difference between Macro cell node and small cell node. Hence, cell age users are mainly affected by high power interference originated from Macro node. The paper develops enhanced inter-cell interference coordination using Almost Blank Sub-Frame (ABS) to improve user throughput. This can be achieved by stopping data transmission on in certain sub frame of macro cell while Pico users are scheduled at the same frame. computer simulation results depict that the performance of Pico cell users is highly degraded, especially for cell edge users. The ABS technique improves cell edge user throughput of Pico cell by 95% and center cell user throughput by 75.25%. © 2024 IEEE.

关键词： Heterogeneous networks

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A Systolic Neural CPU Processor Combining Deep Learning and General-Purpose Computing with Enhanced Data Locality and End-to-End Performance

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IEEE Journal of Solid-State Circuits 2023年第1期58卷 216-226页

作者： Ju, Yuhao Gu, Jie Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

While neural network (NN) accelerators are being significantly developed in recent years, CPU is still essential for data management and pre-/post-processing of accelerators in a commonly used heterogeneous architecture, which usually contains an NN accelerator and a processor core with data transfer performed by direct memory access (DMA) engine. This work presents a special neural processor, referred to as a systolic neural CPU processor (SNCPU), which is a unified architecture combining deep learning and general-purpose computing for fifth-generation of reduced instruction set computer (RISC-V) to improve end-to-end performance for machine learning (ML) tasks compared with a common heterogeneous architecture with CPU and accelerator. With 64%-80% processing elements (PEs) logic reuse and 10% area overhead, SNCPU can be configured into ten RISC-V CPU cores. Special bi-directional dataflow and four different working modes are developed to enhance the utilization of deep NN (DNN) accelerator and eliminate the expensive data transfer between CPU and DNN accelerator in existing heterogeneous architecture. A 65-nm test chip was fabricated demonstrating a 39%-64% performance improvement on end-to-end image classification tasks for ImageNet, Cifar10, and MNIST datasets with over 95% PE utilization and up to 1.8TOPs/W power efficiency. © 2022 IEEE.

关键词： Deep neural networks

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Age, Gender and Emotion Detection using Convolutional Neural Networks (CNNs) 15

Age, Gender and Emotion Detection using Convolutional Neural...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Varghese, Kevin Hablani, Ramchand Department of Computer Science & Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Convolutional Neural Networks (CNNs) are used in this study to determine age, gender, and emotion in face analysis. Using a variety of datasets for training, the age diagnosis component accurately predicts age categories by identifying small changes in face features associated with ageing using CNNs' hierarchical feature learning. Large datasets were used to train the gender detection module, which helps facial recognition and marketing applications by automatically extracting gender-specific traits for accurate gender identification. Sentiment analysis and human-computer interaction benefit from the emotion detection framework's improved emotion classification, which is achieved by capturing temporal and spatial correlations in facial expressions. CNNs are effective for face analysis, as evidenced by the model's adaptability and robustness across a range of demographics, as well as by testing and training on a variety of datasets. This paper provides a thorough framework for age, gender, and emotion recognition based on CNN, which holds promise for breakthroughs in a number of sectors depending on facial analysis. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Closed-form solution for scaling a wireless acoustic sensor network

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IET Wireless Sensor Systems 2023年第5期13卷 190-200页

作者： Patel, Kashyap Kovalyov, Anton Panahi, Issa Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX United States

A closed-form solution for localising and synchronising an acoustic sensor node with respect to a Wireless Acoustic Sensor Network (WASN) is presented. The aim is to allow efficient scaling of a WASN by individually calibrating newly joined sensor nodes instead of recalibrating the entire array. A key contribution is that the sensor to be calibrated does not need to include a built-in emitter. The proposed method uses signals emitted from spatially distributed sources to compute time difference of arrival (TDOA) measurements between the existing WASN and a new sensor. The problem is then modelled as a set of multivariate non-linear TDOA equations. Through a simple transformation, the non-linear TDOA equations are converted into a system of linear equations. Then, weighted least squares is applied to find an accurate estimate of the calibration parameters. Signal sources can either be known emitters within the existing WASN or arbitrary sources in the environment, thus allowing for flexible applicability in both active and passive calibration scenarios. Simulation results under various conditions show high joint localisation and synchronisation performance, often compared to the Cramér-Rao lower bound. © 2023 The Authors. IET Wireless Sensor Systems published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Calibration

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Improving Character Recognition in Bangla Handwritten Words: A Two-Stage Single Shot Detector Approach

Improving Character Recognition in Bangla Handwritten Words:...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Pal, Avi Hasan, Md. Sajid Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350350289

Optical Character Recognition (OCR) for handwrit-ten documents is an important task for various applications such as digital archiving, document retrieval, and text-to-speech systems. Handwritten Bangla script, with its complex characters, poses a significant challenge for OCR systems. A two-stage approach using Single Shot Detector (SSD) with transfer learning is proposed to detect and recognize handwritten Bangla words from an image. In the first stage, a pre-trained model is fine-tuned on a custom dataset of 53 Bangla character labels. In the second stage, The character detection model is used as a backbone and fine-tuned on a custom dataset of Bangla handwritten word images, which also has the same 53 labels. Experimental results show that the two-stage approach achieves an F1 score of 82.86% on test set, outperforming traditional image processing techniques. This proposed approach can contribute to the development of more efficient and accurate OCR systems for Bangla documents, with potential applications in digital archiving, document retrieval, and text-to-speech systems. © 2024 IEEE.

关键词： Optical character recognition

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