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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chand, Syed Riyyan Ahmed, Uzair Ismail, T Mohammed Kumar, B.P. Pradeep Chand, Syed Bilal Chaitra, P. Design Atria Institute of Technology Dept. of Computer Science Bangalore India Atria Institute of Technology Dept. of Computer Science Engineering Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (纸本)9798331538538

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards. © 2025 IEEE.

关键词： Marketing

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A Time-varying Velocity Control Strategy for Balancing Exploration and Exploitation Behaviour of a Binary PSO

A Time-varying Velocity Control Strategy for Balancing Explo...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Siddique, Md. Abu Bakkar Islam, Md. Jakirul DUET Gazipur Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Combinatorial optimization problems (COPs) are NP-hard, and their solution space is discrete, which poses a significant challenge for optimization algorithms. Binary Particle Swarm Optimization (BPSO) is wel-known optimization technique that has been widely applied in various type of COPs. However, one of the challenges in BPSO is to balance exploration and exploitation to locate the optimal solution. This paper presents a Time-varying Velocity Control (TVC) strategy to balance exploration and exploitation in a BPSO. The TVC strategy utilizes a time-varying control parameter to adjust the velocity of the particles, allowing for more exploration in the early phases of the optimization process and more exploitation as the search progresses. The proposed strategy is evaluated on a set of benchmark combinatorial optimization problems, and the results demonstrate that it outperforms other commonly used BPSO strategies in terms of both efficiency and solution quality. Furthermore, the TVC strategy is shown to be robust to changes in the problem landscape, making it a promising approach that can be used for solving a wide range of combinatorial optimization problems. © 2023 IEEE.

关键词： Combinatorial optimization

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Unique Approach of Contextual Uniqueness Assessment of Research Papers Using Multi-Label Classification and Similarity Detection 26

Unique Approach of Contextual Uniqueness Assessment of Resea...

引用

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

作者： Tanvir, Sifat Islam, Md. Zihadul Moon Taher, Sidrat Muztahid, Shaikh Adib Biswas, Arpon Shopnil, Md. Shahariar Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Research papers should possess a significant amount of novelty. However, proper paraphrasing of entire paper may not be identified as plagiarism. Therefore, in this work a unique approach of similarity detection technique has been proposed. At first the abstracts are used to classify which area the paper belongs to, then papers of the same area are extracted and then contextual similarity is detected using cosine similarity. For training and testing purposes, two datasets have been created using data from IEEE Xplore. Since, almost every paper has some elements of paraphrased contents, therefore, a maximum acceptable threshold has also been proposed in this paper. Maintaining the maximum threshold, how well cosine similarity was able to distinguish the novel, paraphrased, and fully plagiarised works has also been analyzed. © 2023 IEEE.

关键词： Contextual Similarity Detection Cosine Similarity Deep Learning Multi-Label Classification

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Accurate Detection of Aquatic Debris in Ocean Surfaces using AlexNet Algorithm over ResNet-50 Algorithm 3

Accurate Detection of Aquatic Debris in Ocean Surfaces using...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Kamal, T.M. Bharatha Devi, N. Saveetha School of Engineering Dept. of Computer Science Chennai India

ISBN: (纸本)9798350306989

This research study intends to detect the aquatic debris accurately on Ocean surfaces using Novel AlexNet algorithm in comparison with ResNet-50 algorithm. Novel AlexNet and ResNet-50 are the two groups considered for this research with 20 iterations done for each group whose G-power is 80% and confidence level is 95%. The dataset consists of 724 JPG file format images depicting clear ocean and 324 JPG file format images depicting aquatic debris, including images with noise. This work demonstrates that the Novel AlexNet algorithm achieves an accuracy of 93.77% which identifies objects accurately over the ResNet50 algorithm with an accuracy of 91.47% and there is no statistical significance difference between the Novel Alexnet Algorithm and ResNet-50 Algorithm for the value of p=0.076 (Independent Sample T-test p © 2023 IEEE.

关键词： Debris

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Distributed and Synchronized Multilingual Audio Container for Video Streams 2

Distributed and Synchronized Multilingual Audio Container fo...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Nidagundi, Pradnya S. Udupa, Prajna Dindore, Chinmayee Kothari, Palak Pujari, Nitin V. PES University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347982

This paper presents a distributed and synchronised multilingual audio container for video streams. The system utilises a distributed architecture to separate audio and video streams, allowing for the independent manipulation and synchronization of multiple languages. The proposed solution addresses the challenges of providing efficient video streaming and has the potential to improve accessibility to foreign language educational content for non-native speakers. The proposed approach improves the scalability and flexibility of the system, as well as the overall user experience. The system is evaluated through simulations and real-world implementation, demonstrating its effectiveness in providing a seamless multilingual video streaming experience. The results show that the proposed system can synchronize multiple languages with high accuracy, and the distributed architecture ensures low latency and high scalability. © 2023 IEEE.

关键词： Synchronization

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Relaxing Super Localization Frame Rate Requirements Utilizing a Novel 2D Interpolation Technique

Relaxing Super Localization Frame Rate Requirements Utilizin...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Tuccio, Giulia Afrakhteh, Sajjad Iacca, Giovanni Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

Ultrasound Localization Microscopy (ULM) by-passes the conventional diffraction limit, allowing the imaging of the vasculature at a microscopic resolution through precise localization of intravenously injected microbubbles. Thanks to their resolution, ULM generated images hold the promise to be the next diagnostic frontier for diseases that modify the vascular structure, such as cancer. However, deploying ULM in clinical settings is currently limited by the prolonged acquisition times and high frame rates needed. To relax the high frame rate requirement, we propose a new time-efficient ULM (TEULM) pipeline. The proposed TEULM introduces the radial basis functions (RBFs) as interpolators upstream of the ULM framework, to recover the missing information consequence of a reduced frame rate. We test TEULM using in vivo Rat's Brain and Kidney datasets acquired at a high frame rate (1 KHz). The results confirm the ability of TEULM to reconstruct super-resolution (SR) images with a similarity score (Dice score) of 80% for both datasets when simulating a frame rate acquisition of 100 Hz;i.e., we can relax the frame rate requirement for ULM by a factor 10 while keeping the SR image quality acceptable. © 2023 IEEE.

关键词： Interpolation

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A Feature Selection-Based Ensemble Machine Learning Method for Predicting Chronic Kidney Cancer

A Feature Selection-Based Ensemble Machine Learning Method f...

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2024 IEEE International Conference on Biomedical engineering, computer and Information Technology for Health, BECITHCON 2024

作者： Bhuiyan, Md. Tofael Ahmed Bhuiyan, Md Nyem Hasan Uddin, Khandaker Mohammad Mohi Based, Md Abdul Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

ISBN: (纸本)9798331534356

Chronic kidney cancer (CKC) is a real-world example of a crucial class of frontiers that are behind basic medical diagnostics and have a significant worldwide impact. For this reason, we need to evaluate more sophisticated prediction algorithms. In order to balance the previously proven CKC detection, this research has presented a novel strategy that combines ensemble approaches with robust machine learning. We employ nine alternative algorithms three from each of Random Forest, Decision Tree, and Light Gradient Boosting Machine in addition to the standard predictive models for this specific stacking ensemble. We obtained an accuracy of 99.19% after preprocessing the data and using collaboration for feature selection. Our model performs with excellent predictive power, similar to Random Forests, but with the ease of de-biasing and good interpretability of Decision Trees. Additionally, as a mixed category, the ensemble performs better than any one model on all performance criteria (precision, recall, and FI score). In order to enhance individual risk prediction of CKC and intervention execution, this adaptable framework is therefore ideal for use in clinical settings. Let's put it this way: our study is a significant step toward improving CKC diagnosis, and it provides a foundation for the broader application of ensemble models of learning in healthcare, which might revolutionize patient care in all specializations. © 2024 IEEE.

关键词： Chronic Kidney Cancer (CKC) Decision Tree Feature Selection HybridModel Machine Learning PredictiveAnalytics Random Forest Stacking Ensemble Technique

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Finding Type 2 Diabetes Using Sparse Balanced Support Vector Machine in Electronic Health Records

Finding Type 2 Diabetes Using Sparse Balanced Support Vector...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Kaur, Ranjanpal Sekhon, Er. Navjot Kaur Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350331790

Early Type 2 Diabetes (T2D) diagnosis is crucial for optimal integrated therapy and patient follow-up. There has been a huge growth in the quantity of accessible Electronic Health Record (EHR) data, and developments in Machine Learning (ML) methodologies have been as amazing. Maintaining and modelling a huge data set can cause over fitting, model interpretability, and computational expenditure. We used Sparse Balanced Support Vector Machine (SB-SVM) to detect type 2 diabetes in an original EHR dataset (named FIMMG dataset). In particular, we have chosen just those aspects of the EHR collected from a homogeneous group of participants before the diagnosis of T2D, including exemptions, examinations, and medicine prescriptions. The proposed solution is trustworthy compared to other ML and Deep Learning methods utilised by the state-of-the-art to handle this problem. The SB-SVM offers the best prediction performance-computation time tradeoff, according to the data. In addition, the induced sparsity makes the model more interpretable while automatically handling high-dimensional data and the normally uneven distribution of classes. © 2023 IEEE.

关键词： Support vector machines

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Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks 4

Deep Learning-Based Spectrum Sharing for Dynamic Resource Al...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Venu, Nookala Kukreti, Kamlesh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331530402

We conduct an extensive study on deep learning-based spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for 6G networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow's communications. © 2025 IEEE.

关键词： Cognitive radio

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Indonesian Hate Speech and Abusive Tweets Classification with Deep Learning Pre-trained Language Models 6

Indonesian Hate Speech and Abusive Tweets Classification wit...

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6th International Conference on computer and Informatics engineering, IC2IE 2023

作者： Yulianto Darmawan, Bagus Tri Irnawan, Bassamtiano Renaufalgi Suzuki, Yoshimi Politeknik Negeri Jakarta Dept. of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350345162

Hate speech and abusive language on social media can spread massively and escalate into conflict between two or more individuals or parties. Previous studies about multi-label classification to identify whether the tweet contains hate speech, abusive language, or not have been conducted with machine learning techniques. They applied machine learning with a combination of feature extraction and achieved a good result in recognizing the tweet with hate speech content. In this research, we apply two approaches: first, we use vanilla pre-trained models of IndoBERT, IndoBERTweet, and Indonesian RoBERTa;second, we combine the three previous models with CNN. Our experiment shows that our proposed method has better results compared to previous research with the best performance achieved by IndoBERTweet+CNN with 93.9% Accuracy. © 2023 IEEE.

关键词： Abusive CNN Deep Learning Hate Speech IndoBERT IndoBERTweet Indonesian RoBERTa Indonesian Twitter

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