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A framework for decision making to purchase the best product using feature-based opinions

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9873-9887页

作者： Ratmele, Ankur Thakur, Ramesh IET-DAVV M.P. Indore India IIPS-DAVV M.P. Indore India

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. © 2024 – IOS Press. All rights reserved.

关键词： Decision making

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Opinion mining from amazon reviews using dual interactive wasserstein namib beetle generative adversarial network

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Multimedia Tools and Applications 2024年第26期83卷 68209-68230页

作者： Ratmele, Ankur Thakur, Ramesh Thakur, Archana IET DAVV Madhya Pradesh Indore452001 India IIPS DAVV Madhya Pradesh Indore452001 India SCSIT DAVV Madhya Pradesh Indore452001 India SVKM’s NMIMS Indore India

Sentimental analysis, is the study of sentiments which resolves the judgement of customer’s opinions emotions, sentiments and evaluations in terms of the entities like, services, topics, products, and events. In this research work, an innovative opinion mining system called Dual Interactive Wasserstein Namib Beetle Generative Adversarial Network (DIWNBGAN) is introduced. Initially, the data is gathered and pre-processed. Secondly, the necessary features are extracted by optimizing the reviews with the aid of lexicon hybrid N-gram2vector method. Since navigating through a vast array of reviews can be challenging, feature selection serves to diminish the dimensionality of the data. Thirdly, the entropy-kurtosis based feature selection technique is proposed for the dimensionality reduction purpose. The selection of an appropriate feature is crucial in sentiment analysis as it enables the identification of product attributes that consumers either dislike or extensively discuss. Finally, the DIWNBGAN is employed to classify the selected reviews into different states, like extremely positive, positive, neutral, extremely negative, and negative. The introduced approach is assessed by applying data collected from amazon’s smartphone review dataset. A total of 150,000 reviews/samples were extracted from web pages and the dataset size is 3200 × 5. The implementation of the introduced work is carried out using PYTHON software. From the experimental outcomes, the achieved accuracy, precision, recall, and F1 Score of the introduced system are: 99.98%, 97.88%, 99.97%, and 98.97% respectively. The proposed model (DIWNBGAN) outperforms all the implemented models based on accuracy, with an improvement of 25.34%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Advancing Foliar Disease Detection in Apple Orchards: Evaluating the Efficacy of the EfficientNetB3 Model in Machine Learning-Based Classification

Advancing Foliar Disease Detection in Apple Orchards: Evalua...

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2024 International Conference on Advances in Computing Research on Science Engineering and Technology, ACROSET 2024

作者： Dembla, Naresh Yadav, Ravindra Iips Davv Computer Department Indore India Iet Davv It Department Indore India

ISBN: (纸本)9798350388800

The performance of a machine learning model in identifying foliar diseases in apple trees is investigated in this work. 23,000 high-quality RGB photos of apple foliar diseases were used to train, verify, and test the model, which is based on the EfficientNetB3 architecture. The model performed admirably throughout training, with a low loss of 0.292115 and a high accuracy of 0.984884. Similar performance metrics were seen in the test and validation stages, with accuracies of 0.875465 and 0.875492 and losses of 0.636863 and 0.654590, respectively. A classification report was also produced in order to evaluate the model's effectiveness for each of the illness groups separately. Out of all of them, 'Healthy' had the best recall (0.97) and accuracy (0.98), indicating that it was able to correctly identify and collect healthy leaves. On the other hand, the illness category 'rust frog_eye_leaf_spot' had the lowest accuracy, recall, and F1-score (all 0.00), suggesting considerable difficulties in accurately categorizing it. The inverse class frequency approach was employed in the study to tackle the problem of class imbalance within the dataset. This method gives each class a weight that is determined by the inverse of how often that class occurs in the training set. In order to lessen the effects of class imbalance and enhance the model's capacity to correctly identify uncommon classes, the method incorporates these weighted class frequencies into the training process. the study shows how well the model works to identify different foliar diseases that affect apple trees, but it also points out key areas that still need work, especially when it comes to resolving the challenges related to certain disease classes' classification. The performance of the proposed model has been compared with SOTA and got to be the highest accurate model. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Solar Power Forecasting Using Machine Learning Techniques 5

Comparative Analysis of Solar Power Forecasting Using Machin...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Mittal, Amit Kumar Mathur, Kirti IET-DAVV Computer Engineering Madhya Pradesh Indore India IIPS-DAVV Madhya Pradesh Indore India

ISBN: (纸本)9798350379945

The research focuses on the development of a solar power forecasting system using machine learning techniques, with a primary focus on the intricate task of accurately predicting solar power generation. Drawing from historical data, the research employs various machine learning algorithms, including K-Nearest Neighbours (KNN), Gradient Boosting Regression Trees (GBRT), Long Short-Term Memory (LSTM), Multiple Layer Perceptron (MLP) to achieve highly precise forecasts. The proposed method includes data pre-processing and including techniques such as standardization and normalization. This ensures that the data is in an optimal state for utilization in machine learning models. This research study highlights the importance of data quality, as well as the reliability and availability of data from external sources, emphasizing their pivotal role in enhancing the accuracy of solar power predictions. This study utilizes data preprocessing, thorough model evaluation, and data visualization techniques for comprehending data patterns and results. © 2024 IEEE.

关键词： Adversarial machine learning

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OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews

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SOCIAL NETWORK ANALYSIS AND MINING 2022年第1期12卷 1-16页

作者： Ratmele, Ankur Thakur, Ramesh IET DAVV Indore Indore Madhya Pradesh India IIPS DAVV Indore MP Indore Madhya Pradesh India

In recent decades, sellers and merchants have asked their customers to share their opinion on the products at online marketplace. Analysis of the massive amounts of reviews for a potential customer is an immense challenge, to decide whether to purchase a product or not. In this paper, a hierarchical attention network-based framework is presented to resolve this challenge. In this proposed framework, the Amazon's Smartphone review dataset is preprocessed using NLP approaches and then applied Glove embedding to extract word vector representation of reviews which identifies contextual information of words. These word vectors are fed into the hierarchical attention network, which produces vectors at the word and sentence levels. Bi-GRU model encodes the words and sentences into hidden vectors. Finally, reviews are classified into five opinion classes like extremely positive, positive, extremely negative, negative and neutral. Furthermore, to perform the experiments with the proposed method, the dataset is divided into three parts such as 80% train, 10% validation and 10% test. Experiments reveal that the proposed framework outperforms baseline methods in terms of accuracy, precision and recall. The OpExHAN model achieved admirable results like 94.6% accuracy, 91% both precision and recall after a lot of hyper-parameter experimentation.

关键词： Opinion extraction (OE) Deep learning (DL) Natural language processing (NLP) Glove Gated recurrent network (GRU) Hierarchical attention network (HAN)

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Fusing facial and speech cues for enhanced multimodal emotion recognition

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1397-1405页

作者： Tomar, Pragya Singh Mathur, Kirti Suman, Ugrasen School of Computer Science and Information Technology (SCSIT) DAVV M.P. Indore India International Institute of Professional Studies (IIPS) DAVV M.P. Indore India

Emotion recognition is a technology that enables computers to recognize and interpret human emotions by analyzing facial expressions, voice, text or physiological signals. It finds applications in human–computer interaction, mental health assessment, and personalized content recommendation, offering insights into user sentiment and engagement. In this paper, we introduced an innovative approach to emotion recognition which combines facial expressions and speech cues within a multimodal system. This fusion of two distinct modalities is achieved through two specific methods: feature-level fusion and decision-level fusion. To evaluate the effectiveness of our approach, we conduct experiments using the eNTERFACE'05 dataset. Our comparative analysis reveals that the integration of fusion-based techniques can substantially enhance the performance of emotion recognition systems. Additionally, our findings highlight the superiority of feature-level fusion over decision-level fusion in terms of overall performance. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Affective computing Facial emotion recognition Human Computer Interaction (HCI) Multimodal emotion recognition Speech emotion recognition

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Assessing the Vulnerabilities of Future Generation IoT Devices: A Security Testbed Approach

Assessing the Vulnerabilities of Future Generation IoT Devic...

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International Conference on Computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Koranne, Sonali Saxena, Avani John, Praful Computer Science DAVV University Takshshila Parisar Bhawarkua Indore India Information Technology IIPS DAVV Takshshila Parisar Bhawarkua Indore India

ISBN: (纸本)9781839539848

Future Generation Internet of Things (FGIoT) device proliferation creates unprecedented security issues while also providing disruptive opportunities across several industries. Through a Security Testbed Approach, this research paper addresses the crucial issue of addressing vulnerabilities within these cutting-edge IoT devices. Our research presents a ground-breaking methodology that is based on the creation and implementation of a comprehensive security testbed with a wide variety of Future Generation IoT devices that represent various application areas. This strategy uses a variety of methods and methodologies to cover device selection, controlled experimentation, and vulnerability assessment. The thorough study we performed revealed significant flaws in these devices, exposing possible hazards that can endanger data confidentiality, integrity, and service availability in Future Generation IoT networks. The practical ramifications of these vulnerabilities are examined, and several mitigating techniques are *** study makes a substantial contribution to the field of IoT security by providing a thorough and repeatable method for evaluating vulnerabilities in Next Generation IoT Devices. The results underline how urgent it is to address security issues in this new environment and provide a strong basis for subsequent research and commercial initiatives focused at enhancing IoT ecosystem security. © The Institution of Engineering & Technology 2023.

关键词： Testbeds

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Advancing Foliar Disease Detection in Apple Orchards: Evaluating the Efficacy of the EfficientNetB3 Model in Machine Learning-Based Classification

Advancing Foliar Disease Detection in Apple Orchards: Evalua...

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Advances in Computing Research on Science Engineering and Technology (ACROSET), International Conference on

作者： Naresh Dembla Ravindra Yadav Computer Department IIPSDAVV Indore India IT Department IETDAVV Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The performance of a machine learning model in identifying foliar diseases in apple trees is investigated in this work. 23,000 high-quality RGB photos of apple foliar diseases were used to train, verify, and test the model, which is based on the EfficientNetB3 architecture. The model performed admirably throughout training, with a low loss of 0.292115 and a high accuracy of 0.984884. Similar performance metrics were seen in the test and validation stages, with accuracies of 0.875465 and 0.875492 and losses of 0.636863 and 0.654590, respectively. A classification report was also produced in order to evaluate the model's effectiveness for each of the illness groups separately. Out of all of them, “Healthy” had the best recall (0.97) and accuracy (0.98), indicating that it was able to correctly identify and collect healthy leaves. On the other hand, the illness category “rust frog_eye_leaf_spot” had the lowest accuracy, recall, and F1-score (all 0.00), suggesting considerable difficulties in accurately categorizing it. The inverse class frequency approach was employed in the study to tackle the problem of class imbalance within the dataset. This method gives each class a weight that is determined by the inverse of how often that class occurs in the training set. In order to lessen the effects of class imbalance and enhance the model's capacity to correctly identify uncommon classes, the method incorporates these weighted class frequencies into the training process. the study shows how well the model works to identify different foliar diseases that affect apple trees, but it also points out key areas that still need work, especially when it comes to resolving the challenges related to certain disease classes' classification. The performance of the proposed model has been compared with SOTA and got to be the highest accurate model.

关键词： Training Measurement Accuracy Instruments Computational modeling Machine learning Feature extraction Diseases Context modeling

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Comparative Analysis of Solar Power Forecasting Using Machine Learning Techniques

Comparative Analysis of Solar Power Forecasting Using Machin...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Amit Kumar Mittal Kirti Mathur Computer Engineering IET-DAVV Indore Madhya Pradesh India IIPS-DAVV Indore Madhya Pradesh India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

关键词： Accuracy Standardization Nearest neighbor methods Boosting Data models Reliability Forecasting Long short term memory Regression tree analysis Meteorology

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A Review on forecasting the photovoltaic power Using Machine Learning

A Review on forecasting the photovoltaic power Using Machine...

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International Conference on Smart Technologies for Sustainable Development 2021, ICSTSD 2021

作者： Mittal, Amit Kumar Mathur, Kirti Mittal, Shivangi IET DAVV Madhya Pradesh Indore India IIPS DAVV Madhya Pradesh Indore India Govt. Polytechnic College Madhya Pradesh Dewas India

In this review paper on different forecasting method of the solar power output for effective generation of the power grid and proper management of transfer rate of energy per unit area occurred into the solar PV system. Essential part in focusing the prediction of solar power is irradiance and temperature. The irradiance can be forecasted by many algorithm and method is applied in prediction of generation of Short-term photovoltaic power and long term solar power forecasting. And many papers describes on numerical weather forecasting and some algorithm like neural networks or support vector regression for two step approach for predicting the PV power. In this review shown that methods like Bagging Model, deep learning, genetic algorithm, random forest, gradient boosting and artificial neural network. We found that for enhancing the performance of predicting PV power many authors proposed the ensemble method that is the hybrid models of different algorithm. And I found that on this review process ensemble methods show that good results and improve the forecasting solar PV power. © Published under licence by IOP Publishing Ltd.

关键词： Solar energy

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