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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

ISBN: (纸本)9783031781650

Developing a real-time sentiment analysis application that relies solely on features extracted from images or textual content falls short of capturing human emotions’ nuanced and multifaceted nature. The unlabeled dataset, though useful, has limitations for sentiment analysis due to its general image descriptions, which lack emotional depth and do not include direct sentiment labels. Finding scene sentiment is a challenging task. To address this, combining textual descriptions with visual features is crucial. Important parameters include entropy, bag of words, and parts of speech (nouns, adjectives, and verbs) for textual analysis, alongside visual features like SIFT, SURF, and color histograms. These features are integrated to capture a comprehensive range of sentiment cues, enhancing the accuracy and depth of sentiment insights. This paper proposes an optimized adaptive neuro-fuzzy inference system for a compelling feature enhancement using the Whale-Honey Badger Optimization Algorithm (WHBOA). The proposed method identifies the most relevant and effective features from both textual and visual data. It captures visual-specific attributes to provide a richer and more detailed representation of visual content, addressing the limitations of general image descriptions and paving the way for the development of predictive models. Additionally, text pre-processing cleans and normalizes the textual data. We conducted an extensive comparative performance evaluation to assess the effectiveness and accuracy of the proposed model. The model is compared with the Nearest Neighbor, Support Vector Machine (SVM), and Decision Tree classification algorithms for the performance *** results demonstrate that the optimized model performs better, achieving an accuracy of approximately 91.2%, compared to the other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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AI-Powered Personalization in E-Commerce

AI-Powered Personalization in E-Commerce

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Aarti Chaudhari Shital Hajare Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The nature of e-commerce is constantly changing how the business is carried out and how things are bought. It has been made possible to do so because of technological advancements and the capacity of the internet that goes as far as allowing the customers to view, buy and get products from the ease of their homes. The idea of e-commerce has developed to cut through both the retail environment and the interface of customer expectations. This is the outcome of artificial intelligence being adopted by the e-commerce sector, namely in the area of customization. More specifically, when AI algorithms are applied, machine learning is becoming more and more significant in e-commerce customization. AI technologies keep developing further and further which enables to analyse the detailed information about the shopping habits, interests and preferences of every client so that every e-commerce unit may be able to cater to every need of a customer. This document focuses on the AI methods related to the e-commerce personalization processes: machine learning, deep learning, and natural language processing (NLP) methods. It focuses on the procedures of gathering and analysing data because these are crucial in understanding the buying patterns of consumers and what they prefer.

关键词： Industries Machine learning algorithms Education Medical services Market research Fatigue Natural language processing Electronic commerce Stress Business

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Segmentation of Lung Images Using Hybridization of Fuzzy C Means and Morphological Techniques

Segmentation of Lung Images Using Hybridization of Fuzzy C M...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Harshini, I. Sri Dharshini, R. Shanmugalatha, A. Saranya, R. Devaki, M. Josephin Shermila, P. Velammal College of Engineering and Technology Department of Electrical and Electronics Engineering Madurai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541170

One of the serious illnesses that could shorten a patient's life is lung cancer. In addition, it is challenging to detect lung cancer early on and most cases are discovered after the disease has spread to other lung regions. The lung cancer tumoral tissue must be located by radiologist and other medical specialists. Consequently, the suggested work makes it easier to segment tumoral tissue from the CT lung image. Lung image segmentation is a crucial task in the field of medical image analysis because it allows lung abnormalities to be recognized and described. In order to achieve better performance hybrid strategies - which combine several approaches - have grown in popularity recently. Morphological operations are combined with fuzzy c-means clustering in this particular hybrid approach. In this work we provide a state-of-the-art overview of lung image segmentation using hybrid fuzzy c-means and morphological approaches. Hybrid techniques are an intriguing topic for additional research because it has been observed that their application can lead to increased resilience and accuracy when analyzing lung images. © 2024 IEEE.

关键词： Lung cancer

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

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Automatic Human Life Rescue Analysis Model with Incorporation of Deep Learning Algorithm 5

Automatic Human Life Rescue Analysis Model with Incorporatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kalaiselvi, V.K.G. Prithikhaa, S. Divya Darshini, J. Reddy, S Bharath Anandkumar, B. Hariharan, Shanmugasundaram Sri Sairam Engineering College Department of Information Technology Tamilnadu India KG Reddy College of Engineering and Technology Department of CSE Telangana India Vardhaman College of Engineering Department of Artificial Intelligence and Machine Learning India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331530013

In today's fast-paced world, every passing moment can mean the difference between life and death. To address this critical issue, we have developed an innovative application designed to provide rapid emergency medical response. Ambulance services play a pivotal role in emergency healthcare by offering vital transportation and care. However, limited access to ambulance contact information often hampers patients' ability to secure timely assistance during emergencies. With the widespread adoption of smartphones and mobile applications, the proposed solution integrates React Native with a Convolutional Neural Network (CNN) to create an advanced mobile app for law enforcement, capable of real-time car accident detection. Utilizing datasets from Kaggle, images are resized to optimize CNN training. Through transfer learning, the CNN architecture effectively extracts spatial features, enabling accurate accident prediction, validated by robust evaluation metrics such as precision and recall. The React Native app facilitates swift notifications to law enforcement, utilizing cutting-edge deep learning technology to enhance road safety and improve emergency response efficiency. This project aims to streamline emergency services, minimizing response and transport times, thereby saving lives and fostering safer communities. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-scale detector optimized for small target

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Optoelectronics Letters 2024年第4期20卷 243-248页

作者： ZHU Yongchang YANG Sen TONG Jigang WANG Zenghui School of Electrical Engineering and Automation Tianjin University of TechnologyTianjin 300384China Center of Artificial Intelligence and Data Science University of South AfricaFlorida 1709South Africa Department of Electrical Engineering University of South AfricaFlorida 1709South Africa

The effectiveness of deep learning networks in detecting small objects is limited,thereby posing challenges in addressing practical object detection *** this research,we propose a small object detection model that operates at multiple *** model incorporates a multi-level bidirectional pyramid structure,which integrates deep and shallow networks to simultaneously preserve intricate local details and augment global ***,a dedicated multi-scale detection head is integrated into the model,specifically designed to capture crucial information pertaining to small *** comprehensive experimentation,we have achieved promising results,wherein our proposed model exhibits a mean average precision(mAP)that surpasses that of the well-established you only look once version 7(YOLOv7)model by 1.1%.These findings validate the improved performance of our model in both conventional and small object detection scenarios.

关键词： networks details thereby

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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COVID-19 Prediction Model Empowered with Fused Computational intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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