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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Lu, Yun Kausar, Tasleem Wang, Xiaoye School of Computer Science and Engineering Huizhou University Huizhou China Mirpur Institute of Technology Mirpur University of Science and Technology Mirpur Pakistan

ISBN: (纸本)9798350350890

Speech enhancement is essential to front-end speech signal processing, which can effectively improve speech quality and clarity. Complex spectral masking and complex spectral mapping are the main methods for speech enhancement in the time-frequency domain. This paper proposes a novel fusing complex spectral masking and complex spectral mapping networks (CSM2Net) to combine both advantages. The complex spectral masking and spectral mapping branches share the same encoder and interact in the decoding stage through a dual-branch cross-attention block. In the encoding and decoding stage, a novel soft-threshold attention block extracts speech time-frequency features and makes the model focus on more critical features. Finally, the outputs of the two branches are fused by a weight-generating block, which efficiently selects the better spectrum. On the test set, CSM2Net achieves advanced performance using almost minimal model parameters compared to other state-of-the-art models. © 2024 IEEE.

关键词： Decoding

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K-Anonymization approach for privacy preservation using data perturbation techniques in data mining

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Materials Today: Proceedings 2022年 64卷 578-584页

作者： Kiran, Ajmeera Shirisha, N. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Nowadays, privacy is a big issue for everyone because the internet generates and shares massive amounts of data every day. Because traditional privacy-preserving techniques do not protect sensitive data well enough, malicious attacks on sensitive data are possible. Traditional methods may have many drawbacks due to various attacks on sensitive data. Thus, data must be preserved before being shared with others. K-Anonymity protects privacy. By definition, anonymization means that a person is not identifiable, traceable, or reachable. Anonymization using k-anonymity protects data privacy and protects entire datasets. The traditional data perturbation method only works with numerical data, but the proposed method is designed to work with categorical data. The Proposed Framework perturbs data for multiple columns at once. The proposed framework's performance is evaluated in two ways. Initially, it keeps the data mining model accurate. Second, it protects the original data privacy while minimizing data loss. The main motivation for using k-anonymity is to remove or transfer personally identifiable information. © 2022

关键词： Data mining

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A Solution to finding Fruit Freshness Index using computer Vision 5

A Solution to finding Fruit Freshness Index using Computer V...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Raja, S Ashwin Srivatsan, T.R. Vijay Maragatham, G. Srmist Ktr Computer Science with Ai and Ml Chennai India Srm Institute of Science and Technology Ktr Department of Computational Intelligence India

ISBN: (纸本)9781665475242

Fruits have now become a product to the market's eye, as fruits being the top recommended food to be consumed for better health, the prices drive higher and higher day by day. The main reason for the increase in the price of fruits is the manual labor behind it. It is the sorting and grading done by such humans which takes a prolonged period of time, inconsistent, onerous, subjective, variable, costly and influenced by other factors such as the environment around them which holds these fruits at an untouchable rate to the normal low paid consumers. To tackle this and hopefully reduce the rising price in fruits we have come up with a solution that a Fruit freshness System needs to be put in place. As computer vision has been used by many researchers to sort and grade fruits, we decided the best way to aid our solution would be by the implementation of computer Vision. The summary of this paper is that it provides an overview of the many methods which have been implemented to reduce and lower the manual labor in the grading and sorting of fruits in the market i.e., feature extraction, segmentation, preprocessing, classification which addresses the fruits and the quality of the fruits based on texture, color, shape, size, and it's defects. These steps lead to a much more accurate shelf-life date of the fruits which also tackles the problem of food wastage, this will decrease the wastage of fruits from turning spoiled much earlier than expected. © 2023 IEEE.

关键词： Fruits

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DLIoMT: Deep Learning Approaches for IoMT-Overview, challenges and the future

DLIoMT: Deep Learning Approaches for IoMT-Overview, challeng...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Harshni, V. Singh, Satyendr Indhumathi, R. Kumar, Neelam Sanjeev Chowdhury, Subrata Patra, Sudhansu Shekhar Srm Institute of Science and Technology Department of Bme Ramapuram campus Chennai India Bml Munjal University Computer Science & Engg Department Gurugram India Vadapalani Campus Chennai India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Deemed to be University School of Compter Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine learning, conventional neural networks, recognition, and fuzzy logic are a few of the subsets of AI with unique capabilities and functions that can enhance modern medical science. Such innovations simplify human interference in clinical diagnostic procedures, decision making, and medical imaging. To improve human health, connected biomedical network devices and software applications are interconnected to form the Internet of Medical Things (IoMT), the next generation of bioanalytical tools. This review paper provides in-depth analysis of the IoMT encasing its definition, technologies, applications, challenges, and future prospects. © 2025 IEEE.

关键词： Electronic health record

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A smart agriculture framework for IoT based plant decay detection using smart croft algorithm

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Materials Today: Proceedings 2022年 62卷 4758-4763页

作者： Gupta, Bhavya Madan, Gazal Quadir Md, Abdul School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Internet of things (IOT) is an assuring technology which provides systematic and logical solutions towards the revolution of various realms. Various researches and analysis have been conducted and numerous methods have been incorporated to apply IoT technology on agronomical fields. IoT can play a vital role in timely detection of declining plant health so that appropriate measures can be taken. It is a huge step towards smart agriculture. In this paper we propose a model to build up an automated framework which will recognize the crop decay in the initial phase which is imperceptible to naked human eyes. This model helps in prevention of huge losses and also save a lot of time and labor. The proposed model builds a recognition framework using sensors like humidity, moisture, temperature and color of the plant leaf. The data from the sensors is sent to Arduino to Cloud which then analyzes the data and helps in identifying the plant decay. In the upcoming years, the internet of things will be a crucial bit in the smart farming system. © 2022

关键词： Moisture

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Efficient Business Meeting Summarization: Leveraging Natural Language Processing for Enhanced Productivity 5th

Efficient Business Meeting Summarization: Leveraging Natural...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Shanthamalar, J. Jeslin Nithya, S. Kumar, Alle Nithin Chandra, Beraboina Jaya Nancy Noella, R.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819788644

In the contemporary corporate landscape, efficient utilization of time is paramount for organizational success. Business meetings, a fundamental component of decision-making processes, often result in copious amounts of information exchange. This project aims to streamline and enhance the post-meeting experience through the development of an Automated Business Meeting Summarization system. Additionally, the system employs sentiment analysis to gauge the overall tone and mood of the meeting, providing valuable insights into team dynamics and engagement levels. The project aims to empower organizations with an efficient tool that not only accelerates the decision-making process but also fosters better collaboration and understanding among team members. In contrast, zero-shot Whisper models demonstrate a robustness frontier that includes the 95% confidence interval for human performance. The BART-large-CNN-SAMSum model demonstrates promising performance on the SAMSum summarization dataset, achieving a ROGUE-1 score of 42.621. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sentiment analysis

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Deepfake Detection: Leveraging InceptionResNetV2 and LSTM for Enhanced Accuracy

Deepfake Detection: Leveraging InceptionResNetV2 and LSTM fo...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Saini, Aradhna Attiuttama Naaz, Sheenam Shivhare, Anushka Dhuriya, Gaurav Yadav, Neha G L Bajaj Institute of Technology and Management Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Artificial Intelligence and Machine Learning Greater Noida India Sharda University Computer Science and Engineering Greater Noida India Netaji Subhas University of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The antique adage "seeing is believing"is simply no longer actual, which has profound implications for a ramification of sides of our lives. Deepfakes are becoming less complicated and simpler to create as technology advances. In fact, with the right software program, you can do a part of it right out of your palm. it is challenging to pick out deepfakes. The capability of the human eye to differentiate among deepfakes has reduced. however, a few academics have tried to identify deepfake. Deepfakes are media that can be generated by synthetic intelligence (AI) using algorithms. Artificial intelligence algorithms are designed to understand the traits of both the supply and the target photo. Subsequently, the target image is positioned on the pinnacle of the supply photograph. Our goal is to pick out video deepfakes by way of utilizing deep getting-to-know neural networks which include InceptionResNetV2 and LSTM. With the usage of transfer learning and the pre-trained InceptionResNetV2 CNN for characteristic extraction and vector production, we were able to efficaciously assemble a deepfake detection version. The capabilities had been used to train the LSTM layer, and the confusion matrix that was produced offers us the accuracy of testing and validation. The corresponding version's accuracy for 20 and 40 epochs was 84.75% and 91.48%, respectively. © 2025 IEEE.

关键词： Generative adversarial networks

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Mutual Funds Investment Risk Analysis Using Machine Learning 2

Mutual Funds Investment Risk Analysis Using Machine Learning

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Dasgupta, Anupam Ravuri, Nandhika Jhansi Nallakaruppan, Musiri Kailasanathan Gite, Shilpi Natesan, Deepa Balusamy, Balamurugan Vellore Institute of Technology School of Electronics and Communication Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Information Technology and Engineering Vellore India Symbiosis Institute of Technology Aiml Department Pune India Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur Chennai India Shiv Nadar Institution of Eminence Noida India

ISBN: (纸本)9798350384277

The involvement of machine learning (ML) in the finance sector has ushered in a new era of investment management, profoundly influencing the mutual funds landscape. It has redefined strategies and optimized financial decision-making. This paper provides a comparative analysis of ML technologies such as AdaBoost Regressor, Support Vector Machines, Gradient Boosting, and Random Forest. The positive results indicated by the paper highlight the pivotal role of ML in revolutionizing mutual funds management in India, reflecting the nation's aspirations for financial innovation and inclusion aligning it with global levels of efficiency in investment strategies. The efficiency of the models was tested with performance metrics such as Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Performance Error (MAPE). Our evaluation concluded Random Forest was the best performer with an R2 Score of 0.925, MSE of 0.246, RMSE of 0.496, MAE of 0.271, and MAPE of 0.096. © 2024 IEEE.

关键词： Decentralized finance

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A New Approach to Design Smart Surveillance System by Enhancing Doorbell Mechanism 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Dwivedi, Shashank Tiwari, Amit Kumar Sankalp, Prem Gupta, Pradeep Shubbham Malviya, Abhishek Department of Computer Science and Engineering United Institute of Technology Uttar Pradesh Prayagraj211008 India

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

Smart Doorbell and Locking system is a system of interconnected IoT devices and sensors. This is an IOT device for home or office automation. Traditional doorbells serve the purpose but this smart doorbell can save plenty of time with the help of face recognition module. The purpose of this project is not just limited in saving time but it also offers some safety features. This device is just a step towards smart homes. Although there are plenty of commercial smart doorbells available but they are quite expensive and some of them lacks features that this project offers. This device can connect to Wi-Fi and can be accessed with mobile devices which is time-saving and provides complete control of this device through fingertips. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition IOT OpenCV Raspberry Pi Smart doorbell Smart lock

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Enhanced Pneumonia Detection in Chest X-rays through a Hybrid DL-ML Ensemble Model 3

Enhanced Pneumonia Detection in Chest X-rays through a Hybri...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harshitha, Rasuri Sree, Mudu Navya Kiran Kumar, G. Anila, M. China Ramu, S. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad India

ISBN: (纸本)9798331527549

Pneumonia is a respiratory infection and has occurred in more than 450 million people every year, causing about 2.5 million deaths, especially among infants, the aged, and people with compromised immune systems. It inflames the lungs by filling air sacs with fluid or pus, causing coughing, fever, and labored breathing. It is very essential to diagnose at the right time because pneumonia may be confused with pulmonary tuberculosis and seasonal infections, thus delaying the treatment and raising the risk of death. Currently, diagnostic methods rely on conventional image analysis models such as InceptionResNetV2, Xception, VGG16, ResNet50, and DenseNet121 in addition to transfer learning and standalone algorithms. However, these approaches lack the accuracy required for the detection of pneumonia. This study proposes a hybrid model combining deep learning, especially CNNs, with machine learning classifiers. It enhances specificity and sensitivity in diagnosing pneumonia from CXR images for earlier and more accurate detection. It could lead to a major decrease in mortality associated with pneumonia based on prompt effective treatment. Feature extraction from the CNN is married with machine learning classification to maximize the diagnostic power. This approach may provide an excellent framework from which future augmentations can focus on severity stratification and also more general types of thoracic diseases. © 2025 IEEE.

关键词： Diagnosis

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