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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 IEEE.

关键词： Convolutional neural networks

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The Multi-class Paradigm: How Transformers Are Reshaping Language Analysis in NLP 3rd

The Multi-class Paradigm: How Transformers Are Reshaping La...

引用

3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Islam, Mohammad Shariful Rony, Mohammad Abu Tareq Saha, Pritom Ahammad, Mejbah Nazmul Alam, Shah Md. Bappi, Jabed Omor Siddiki, Marjuk Ahmed Noakhali3814 Bangladesh Noakhali3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University TempeAZ United States Electrical and Electronic Engineering Port City International University Chittagong Bangladesh

ISBN: (纸本)9789819739363

Language-based multilabel text classification is a crucial topic in natural language processing (NLP). However, unlike English, the development of natural language processing in Bengali is still in its infancy, and there is little research done specifically for the Bengali language, one of the most widely spoken languages in the world. As a consequence, it’s past time to address this issue for effective information management and data structure. This research presents a novel human-annotated Bangla sentence dataset. The dataset was categorized into five classes (assertive, interrogative, imperative, optative, or exclamatory) containing 10,000 sentences based on the function and purpose of Bangla grammar context. Fine-tuning applied six transformer-based pre-trained multilingual and monolingual BERT models to this multilabeled dataset. The study evaluated the effectiveness of various models, including BERT, AlBERT, RoBERTa, DistilBERT, XLNet, and BanglaBERT, in sentence classification tasks. The results showed that BanglaBERT outperformed all other models with an accuracy of 99.03%. The study concluded that BanglaBERT is superior in achieving exceptional accuracy and precision, surpassing the capabilities of other popular machine learning models such as LSTM, RNN, SVC, DT, KNN, and RF, as well as other BERT models of Bangla text classification. BERT-based models have great potential in understanding complex language nuances and context and are critical to advancing natural language processing tasks, especially in Bangla language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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Fraud Detection in Ecommerce Transactions: An Ensemble Learning Approach 23

Fraud Detection in Ecommerce Transactions: An Ensemble Learn...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Anjali Singh Masters in Computer Science and Engineering Galgotias University India

ISBN: (纸本)9798400709418

One of the most critical factors that regulators and consumers consider when it comes to assessing the trustworthiness and security of online transactions is the detection of fraud. This paper presents a framework that combines the power of various machine learning models to perform fraud detection. Due to the increasing sophistication of the techniques used in this field, single-model approaches are typically not able to provide the best possible performance. With the help of ensembles learning, which combines multiple prediction models, an effective solution can be obtained. The study compares the performance of different models in the detection of fraud carried out on e-commerce transactions. The evaluation of these models is carried out using various performance metrics, such as F1-score, precision, and recall. The results of the study revealed that the XGBoost model performed better than the other ensembles when it comes to detecting fraud. Its high F1-score and accuracy can be attributed to its efficient gradient boosting implementation. Compared to traditional GBM, XGBoost's model formulation delivers better performance and control overfitting. The recommendations from this research can help improve the efficiency and effectiveness of e-commerce fraud detection systems, protect the interests of consumers and businesses, and help prevent fraudulent activities.

关键词： E-commerce transactions

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Elliptic Curve Diffie-Hellman based Privacy-Preserving Deduplication for Big Data in Cloud Systems

Elliptic Curve Diffie-Hellman based Privacy-Preserving Dedup...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Madasu, Sairam Murugesan, Prakash Jaganathan, Humashankar Vellathur Pamulaparthyvenkata, Saigurudatta Masters of Science in Computer Science Cloud Computing and Artificial Intelligence United States Artificial Intelligence and Data Verizon United States Cbl Cgq Atlanta United States Big Data Independent Researcher United States

ISBN: (纸本)9798350360660

Data deduplication is crucial for cloud computing, particularly in managing the expansive growth of outsourced mobile data. Some existing approaches are vulnerable to brute-force attacks and single-point attacks, posing significant security risks. Furthermore, it causes substantial complexity on the resource-constrained edge nodes and the lack of support for cross-domain deduplication. To overcome this problem, this research aims to propose an effective and secure big data deduplication approach in cloud computing. This research proposes the novel combined encryption algorithm of the Elliptic Curve with Diffie Hellman (ECDH) for data privacy preservation and deduplication. The effectiveness and the security of the ECDH approach permit it to manage the enhanced data volumes in the multi-domain framework without significant performance degradation. The experimental results show that the proposed ECDH approach attains the effective communication overhead of data upload and data retrieval of 5.98Kb and 1.39Kb on the data size of 2Kb respectively as compared to the existing methods EC and DH. © 2024 IEEE.

关键词： Differential privacy

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network 7

The New Frontier in Media: AI-Driven Content Creation for Ad...

引用

7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Comcast Software Development and Engineering in Content Discovery and Ai United States Troy University Masters in Computer Science AL. Department of Computer Science Montgomery Campus United States Dt Amtrak Sr. Principal Systems Engineer in C and Rs Platform Services United States University of South Alabama Masters in Electrical Engineering Department of Electrical Engineering United States

ISBN: (纸本)9798331505530

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics. © 2024 IEEE.

关键词： Generative adversarial networks

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Beyond Words: Unraveling Text Complexity with Novel Dataset and A Classifier Application 26

Beyond Words: Unraveling Text Complexity with Novel Dataset ...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Mohammad Shariful Rony, Mohammad Abu Tareq Saha, Pritom Ahammad, Mejbah Nazmul Alam, Shah Md Saifur Rahman, Md Computer Science and Telecommunication Engineering Bangladesh Department of Statistics Noakhali3814 Bangladesh Daffodil International University Computer Science and Engineering Dhaka1216 Bangladesh American International University Bangladesh Computer Science and Engineering Kuril Dhaka Bangladesh Masters of Computer Science University:Arizona State University United States Strategic Management University:University of the Cumberlands United States

ISBN: (纸本)9798350359015

Text classification is a fundamental aspect of Natural Language Processing (NLP). This research presents a novel human-annotated English sentence dataset categorized into four classes (simple, complex, compound, complex-compound) containing 22331 sentences and a sophisticated sentence classifier tool offering the capability to analyze and classify sentences within English text with particular relevance to literature writing. This study explores its performance using three distinct feature representation methods: Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word Embedding Features. The study involves the evaluation of four machine learning and two deep learning classifier models. BoW combined with Support Vector Classifier (SVC) and Logistic Regression (LR) demonstrated impressive accuracy rates, excelling in distinguishing sentence complexity. Word Embedding Features, specifically LSTM and RNN, offer a more profound semantic representation. LSTM stands out with the highest accuracy of 98.03% and balanced precision and recall, yielding an average F1-score of 97%. RNN, slightly less accurate at 97.75%, nevertheless exhibits competence in grasping sentence structure dependencies. It offers valuable insights for practical applications and contributes to the broader understanding of sentence structures and semantics. © 2023 IEEE.

关键词： Classifer NLP Performance Sentence Web app

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Tracing Transaction across bitcoin address behaviour for pattern analysis

Tracing Transaction across bitcoin address behaviour for pat...

引用

Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Ankita Parihar Amit Kumar Abhishek Kumar Department of Computer Science Masters of Engineering - CSE Chandigarh University

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

Internet introduce modern digital currencies which have evolved from our fiat currencies. As the world faced a recession driven by centralized assets, an evolution took place. Additionally, banks charged negative interest on tangible property. When money was in danger, the Internet provided a solution that altered the global system of currency ***, the pioneering cryptocurrency that has revolutionized digital finance. An unchangeable record of every transaction is kept through the use of blockchain technology in it. The public disclosure of a cryptocurrency’s whole the formation and transaction history fosters user confidence. People on the Bitcoin network deal with one another using anonymous Bitcoin addresses, never disclosing their true identities. To optimize their privacy and convenience, individuals frequently utilize many distinct email addresses. In this study it also examines academic literature to understand the various uses of Bitcoin. A comprehensive review of existing research is conducted to detail the characteristics and applications of Bitcoin. The paper utilizes primary information from scholarly articles and secondary data from publicly accessible case studies that are relevant to the topic. Beyond developing Bitcoin faces several challenges, including regulatory uncertainty, scalability issues. Although its initial appearance seemed to indicate hope for a better future, now it is hard to predict how bitcoin will emerge.

关键词： Technological innovation Privacy Uncertainty Reviews Online banking Scalability Bitcoin Internet History Pattern analysis

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Opinion Mining and Analysis using Deep Learning 7

Opinion Mining and Analysis using Deep Learning

引用

7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Joshi, Divya Bathla, Gaurav Chandigarh University Masters of Engineering India Chandigarh University Computer Science and Engineering Department Chandigarh India Chandigarh University Department of Computer Science India

ISBN: (纸本)9781665421683

Opinion Mining, though seems to be everyday term in the field of NLP, yet has a lot of milestones ahead, waiting to be achieved. In this paper, focus was to find better "Opinion Mining with analyzed sentiments"Model based on accuracy of different deep learning techniques after comparing accuracy levels of different deep learning models on which experiments were done. Recently Deep Learning Techniques have been adopted for this task, as it has been showing very promising results in terms of accurately deducing the emotion behind a text lying in internet database. In this paper, experiments were done with different techniques deep learning has provided us with, in order to create a better Opinion mining *** the help of the experiments that were conducted, using the well-known IMDb data set, it was found that a combination of carefully curated model of selective layers can produce better results than just adding on CNN layers in hopes of getting results. The model is then tested on other datasets and is found to be better than other similar state-of-the-art models. © 2022 IEEE.

关键词： Sentiment analysis

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques 3

Enhancing Medical Image-Based Diagnostics Through the Applic...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Byeon, Haewon Nair, Rajit Mahalakshmi, V. Khalaf, Mohammed I. Kaushik, Bharat Shabaz, Mohammad Inje University Department Of Ai And Software Gimhae50834 Korea Republic of School Of Computing Science & Engineering Vit Bhopal University Bhopal India College Of Engineering And Computer Science Jazan University Department Of Computer Science Jazan45142 Saudi Arabia Al Maarif University College Department Of Computer Science Al Anbar 31001 Iraq Masters University Of Northern Iowa IA United States Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques. © 2024 IEEE.

关键词： Diagnosis

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AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Gangul, Ranita Kolekar, Sagar Chintakunta, Advaitha Naidu Mungara, Sudeep Bangalore India Institute of Technology Department of Data Science Stevens HobokenNJ07030 United States Delaware State University Computer Science & Engineering Bangalore India Department of Civil Engineering Pune India University of North Carolina at Charlotte Department of Computer Science CharlotteNC28223 United States Masters in Information Systems Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9798331508685

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity. © 2025 IEEE.

关键词： Risk assessment

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