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International Conference on Ubiquitous Intelligent Systems, ICUIS 2021

作者： Hari Narayanan, A.G. Amar Pratap Singh, J. Department of Computer Application Noorul Isalm Centre For Higher Education Kumaracoil Thuckalay Kanyakumari Tamilnadu629180 India Department of Computer Science and IT Amrita School of Arts and Sciences Kochi Amrita Vishwa Vidyapeetham Kochi India Department of Computer Science and Engineering Noorul Isalm Centre For Higher Education Kumaracoil Thuckalay Kanyakumari Tamilnadu629180 India

ISBN: (纸本)9789811636745

Emotions finding from stories is a wide range area of research with lot of different applications. Through this research work, we are trying to predict the effect of emotion from stories with the help of ensemble classifiers. Stories are an essential part of childhood. It is an effective way for children to understand the environment and things happening around the world. The story telling will help them to develop good manners and intellectual power. It will definitely impact the behavior of children like through the situations described in the story. The basic emotions in kids square measure joy, fear, anger, disgust, surprise, disappointment and neutral. The degree of those emotions depends upon the essential character of kid. Classification is an important data mining technique which is used here to classify the sentences in the stories based on the emotion reflected in the child. Here, we are examining the efficiency of classification algorithms for creating the prediction models from children stories. For that, we are using both single and ensemble classifiers, which help us to make good comparison for story-based emotion experiment because it shows 80% accuracy and takes only less time to build model using both classifiers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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A classifier ensemble approach for prediction of rice yield based on climatic variability for coastal Odisha region of India

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Informatica (Slovenia) 2021年第3期45卷 367-380页

作者： Mishra, Subhadra Mishra, Debahuti Mallick, Pradeep Kumar Santra, Gour Hari Kumar, Sachin Department of Computer Science and Application CPGS Odisha University of Agriculture and Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha’O’ Anusandhan Deemed to be University Odisha Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Soil Science and Agricultural Chemistry IAS Siksha’O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science South Ural State University Chelyabinsk Russia

Agriculture is the backbone of Indian economy especially rice production, but due to several reasons the expected rice yields are not produced. The rice production mainly depends on climatic parameters such as rainfall, temperature, humidity, wind speed etc. If the farmers can get the timely advice on variation of climatic condition, they can take appropriate action to increase the rice production. This factor motivate us to prepare a computational model for the farmers and ultimately to the society also. The main contribution of this work is to present a classifier ensemble based prediction model by considering the original rice yield and climatic datasets of coastal districts Odisha namely Balasore, Cuttack and Puri for the period of 1983 to 2014 for Rabi and Kharif seasons. This ensemble method uses five diversified classifiers such as Support Vector Machine, k-Nearest Neighbour, Naive Bayesian, Decision Tree, and Linear Discriminant Analysis. This is an iterative approach;where at each iteration one classifier acts as main classifier and other four classifiers are used as base classifiers whose output has been considered after taking the majority voting. The performance measure increases 95.38% to 98.10% and 95.38% to 98.10% for specificity, 88.48% to 96.25% and 83.60% to 94.81% for both sensitivity and precision and 91.78% to 97.17% and 74.48% to 88.59% for AUC for Rabi and Kharif seasons dataset of Balasore district and also same improvement in Puri and Cuttack District. Thus the average classification accuracy is found to be above 96%. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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A novel borda count based feature ranking and feature fusion strategy to attain effective climatic features for rice yield prediction

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Informatica (Slovenia) 2021年第1期45卷 13-31页

作者： Mishra, Subhadra Mishra, Debahuti Mallick, Pradeep Kumar Santra, Gour Hari Kumar, Sachin Department of Computer Science and Application CPGS Odisha University of Agriculture and Technology Bhubaneswar Odisha India Department of Computer Science and Engineering Siksha’O’ Anusandhan Deemed to be University Bhubaneswar Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Department of Soil Science and Agricultural Chemistry IAS Siksha’O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Science South Ural State University Chelyabinsk Russia

An attempt has been made in the agricultural field to predict the effect of climatic variability based on rice crop production and climatic features of three coastal regions of Odisha, a state of India. The novelty of this work is Borda Count based fusion strategy on the ranked features obtained from various ranking methodologies. Proposed prediction model works in three phases;in the first phase, three feature ranking approaches such as;Random Forest, Support Vector Regression-Recursive Feature Elimination (SVR-RFE) and F-Test are applied individually on the two datasets of three coastal areas and features are ranked as per the their algorithm. In the second phase;Borda Count as a fusion method has been implemented on those ranked features from the above phase to obtain top five best features. The multiquadratic activation function based Extreme Learning Machine (ELM) has been used to predict the rice crop yield using those ranked features obtained from fusion based raking strategy and the number of varying features are obtained which gives prediction accuracy above 99% in the third phase of experimentation. Finally, the statistical paired T-test has been used to evaluate and validate the significance of proposed fusion based ranking prediction model. This prediction model not only predicts the rice yield per hector but also able to obtain the significant or most affecting features during Rabi and Kharif seasons. From the observations made during experimentation, it has been found that;relative humidity is playing a vital role along with minimum and maximum temperature for rice crop yield during Rabi and Kharif seasons. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

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Gradient Sign Inversion: Making an Adversarial Attack a Good Defense

Gradient Sign Inversion: Making an Adversarial Attack a Good...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaojian Ji Li Dong Rangding Wang Diqun Yan Yang Yin Jinyu Tian Department of Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology Geely Auto Central Research Institute School of Computer Science and Engineering Macau University of Science and Technology

Deep neural networks have been proven vulnerable to deliberately crafted adversarial example, which cause serious safety and security concerns. Many defense approaches were proposed to resist such threats. However, existing defenses such as pre-compression or adversarial training would degrade the model performance on clean images or incur heavy computational costs. In this work, we propose a plug-and-play defensive module Gradient Sign Inversion (GSI) to defend gradient-based attack. Essentially, GSI attempts to inverse the direction of the backpropagated gradient for the victim model, disturbing the adversarial example generation of the attacking while retaining the performance of the vanilla network on genuine inputs. Specifically, an additive model based on periodic trigonometric function is established by investigating the necessary conditions that a suitable defensive module should have. By enforcing constraints on the defensive module, the parameters of GSI are determined, accompanied by a theoretical justification. Interestingly, we observe that the proposed GSI not only prevents the gradient-based adversarial attack, but can even improve the confidence of the ground-truth label when initiating an attack, making the attack betray as a defense. Source code is publicly available at https://***/JidaDiao/GSI.

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Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Shubh Sharma Disha Mohini Pathak Dhyanendra Jain Somya Srivastava Prashant Vats Govind Murari Upadhyay Department of Computer Science ABES Engineering College Ghaziabad U.P India Department of Computer Science Engineering-AIML ABES Engineering College Ghaziabad U.P India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity.

关键词： Deep learning Deepfakes Accuracy Computational modeling Refining Media Benchmark testing Feature extraction Real-time systems Resilience

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DeepDerm: Elevating Precision in Dermatological Diagnosis with Enhanced CNN

DeepDerm: Elevating Precision in Dermatological Diagnosis wi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Monica R Mundada S Seema Sowmya B J Sonia Fathima Student Department of Masters of Compiuter Application Ramaiah Institute of Technology Banglore India Department of Computer Science and Engineering Ramaiah Institute of Technology Banglore India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Banglore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Since human skin illnesses are among the most common dermatological conditions worldwide, visual observations are usually the first step in the diagnosis process. Accurate classification of skin biopsies and scrapings requires dermoscopic analysis and clinical screening due to medical criteria. It gets more difficult to classify skin diseases using medical photographs because of the complex forms, wide range of colors, and data security issues. In this study, we offer a dependable approach for the classification of 20 distinct types of skin illnesses, based on an improved Convolutional Neural Network (CNN) model. Our enhanced CNN design exceeds the standard CNN, which only achieves an accuracy of 31.38%, with an astounding accuracy of 98.21%. The accuracy of two benchmark algorithms, AlexNet and VGG19, is also not particularly high (32.14% and 52.30%, respectively). In contrast to traditional CNN models the enhanced CNN performs better, demonstrating its efficacy and dependability in accurately identifying a wide range of skin disorders. including well-known architectures like AlexNet and VGG19. Positive implications of this discovery include the development of more precise diagnostic tools for dermatology.

关键词： Training Visualization Accuracy computer architecture Skin Classification algorithms Convolutional neural networks Medical diagnostic imaging Standards Diseases

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A Conflicting Evidence Fusion Method Based on Game Theory Combination Weighting

A Conflicting Evidence Fusion Method Based on Game Theory Co...

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computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Ziye Wang Jianyu Xiao School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This essay introduces a new approach to address the challenges of applying Dempster-Shafer evidence theory to tackling high-conflict evidence. We first employed Jousselme’s distance and compatibility coefficients to measure confidence and support, then converted these values into vectors. Next, we applied game theory to convert the vectors into a system of linear equations,from which we obtained a corresponding combination of weight coefficients that we used to modify the initial evidence. Finally, we applied the Dempster-Shafer combination rule to achieve evidence fusion. A case analysis shows that our proposed method provides more sensible fusion results for high-conflict evidence.

关键词： computer vision Accuracy Evidence theory Data integration Reliability theory Vectors Game theory Convergence

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A Comprehensive Evaluation of Face Mask Identification System using Modified Deep Learning Methodology 10

A Comprehensive Evaluation of Face Mask Identification Syste...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Lakshman, Bijee Vanitha, R. Vasuki, R. Uparkar, Satyajit S. Arun, S. Thiagarajan, R. Women's Christian College PG Department of Data Science College Road Chennai India Jerusalem College of Engineering Department of Cyber Security Chennai India Dhanalakshmi Srinivasan University Department of Cse School of Engineering and Technology Trichy India Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Jerusalem College of Engineering Department of ECE Chennai India Prathyusha Engineering College Department of It Chennai India

ISBN: (纸本)9798350353068

Recent years have seen an explosion in the prevalence, infectiousness, and potential harm posed to humanity by the corona virus family of viruses. When one person breathes out the virus, it lands on surfaces and other people breathe them in, causing the sickness to spread from person to person. As a result, safeguarding ourselves and those close to us has assumed paramount importance. There are measures we may do to protect ourselves, including as keeping our distance from others, wearing a mask, washing our hands often (every two hours), and using hand sanitizer. Masks worn by the general public have recently grown ubiquitous across the globe. The situation in India is particularly severe because of the country's dense population packed into a relatively limited landmass. Using a cross-validation strategy involving the conventional learning model known as Convolutional Neural Network (CNN), this paper presents a method called Modified Learning based Mask Identification Rule (MLMIR) for detecting whether or not a face mask is worn in offices or other places with a high volume of employees. The model's performance using live video streaming was astonishing once it was trained with a dataset made from real-world data. Using a number of additional parameters and a large number of people placed at different heights and angles inside the picture, we further evaluate the model's accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Fourth-Order Sparse Array Generator Based on Sum-Difference Co-Array Analysis

A New Fourth-Order Sparse Array Generator Based on Sum-Diffe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haodong Guo Hua Chen Hongguang Lin Wei Liu Qing Shen Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Information and Electronics Beijing Institute of Technology China

In this paper, based on sum-difference co-array analysis, a new fourth-order sparse array called sum-difference-FODC (SD-FODC) is proposed, allowing the construction of a fourth-order DCA with long consecutive lags using the continuous segments in the second-order DCA and SCA of the original array. It has a closed-form expression for sensor positions that can be generated by two arbitrary nonuniform linear arrays (NLAs) called generators. If the second-order SCA and DCA of the generators have long consecutive segments, the designed fourth-order sparse array can achieve a large number of uDOFs. Numerical results are provided to demonstrate the superior performance of the proposed design.

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A Novel Feature Extraction Model for the Detection of Plant Disease from Leaf Images in Low Computational Devices

arXiv

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arXiv 2024年

作者： Pal, Rikathi Bhaumik, Anik Basu Murmu, Arpan Hossain, Sanoar Maity, Biswajit Sen, Soumya A. K. Choudhury School of Information Technology University of Calcutta Kolkata India Department of Computer Application and Science Institute of Engineering and Management Kolkata India

Diseases in plants cause significant danger to productive and secure agriculture. Plant diseases can be detected early and accurately, reducing crop losses and pesticide use. Traditional methods of plant disease identification, on the other hand, are generally time-consuming and require professional expertise. It would be beneficiary to the farmers if they could detect the disease quickly by taking images of the leaf directly. This will be a time-saving process and they can take remedial actions immediately. To achieve this a novel feature extraction approach for detecting tomato plant illnesses from leaf photos using low-cost computing systems such as mobile phones is proposed in this study. The proposed approach integrates various types of Deep Learning techniques to extract robust and discriminative features from leaf images. After the proposed feature extraction comparisons has been done on five cutting-edge deep learning models: AlexNet, ResNet50, VGG16, VGG19, and MobileNet. The dataset contains 10,000 leaf photos from ten classes of tomato illnesses and one class of healthy leaves. Experimental findings demonstrate that AlexNet with an accuracy score of 87%, with the benefit of being quick and lightweight, making it appropriate for use on embedded systems and other low-processing devices like smartphones. © 2024, CC BY.

关键词： Fruits

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