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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Jaggi, Apar Aggarwal, Akshat Gupta, Ankush Meerut Institute of Engineering and Technology Department of Computer Science and Engineering Meerut India

ISBN: (纸本)9798350346961

Safety is the primary concern in present times. Crimes happen in public places and the criminal can quickly get away from the scene without anyone noticing him or any evidence against him. CCTV cameras are used for surveillance monitoring but they still need human supervision to operate and thus have a higher possibility of human error. So, in such cases, we need a machine to recognize such tasks and create evidence if it notices any such activity. Though many modern and advanced machine learning algorithms, processors, and CCTV cameras are available, but real-time detection is still difficult to achieve. Our work aims to create a system that identifies if any anti- social or abnormal activity is there or not from cluttered scenes. This works on Transfer Learning. We propose to use a Deep Convolutional Network (DCN), a state-of-the-art CNN model using the latest object detection technique YOLOv7. Using this in surveillance monitoring can be useful to reduce both the risk to human life and the rate of crime.. © 2023 IEEE.

关键词： Object detection

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Utilizing Machine Learning Models to Determine the Security Level of Different Cryptosystems 7

Utilizing Machine Learning Models to Determine the Security ...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Pranathi, Kantipudi Priya, Bodepudi Lakshmi Felix, A. Yovan Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397284

Due to recent developments in multimedia technology, digital data security has emerged as an important concern. To improve upon the state of the art in terms of security, researchers often recommend modifications to procedures that have previously been implemented. However, many suggested encryption algorithms have proved unsafe over the previous several decades, putting sensitive data at risk. It is crucial to use the most suitable encryption strategy to defend against such attacks;nevertheless, the type of data being protected might affect the method that is most appropriate for each given situation. However, systematically evaluating various cryptosystems to choose the optimal one may consume significant computational effort. To rapidly and reliably choose the appropriate algorithm, an SVM (support vector machine) is proposed as a security-level identification tool for photo encryption algorithms. In this research, a dataset was compiled with the help of common encryption security criteria, including Security, Peak signal to noise ratio, Homogeneity, Correlation, Contrast, Energy, Entropy, and Mean Square Error. These values are used as extracted characteristics from various cypher pictures. There are three tiers of security for dataset labels: strong, acceptable, and weak. For evaluating the effectiveness of the proposed model, the calculated accuracy and results demonstrate the value of this SVM system. © 2023 IEEE.

关键词： Support vector machines

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Cyber-Ego: The Digital Self 9

Cyber-Ego: The Digital Self

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Saravanan, R. Aishwarya, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

Cyber Ego expands to Cyber and Alter-ego. In this proposed paper, I have integrated four deep learning models for performing the tasks - natural language generation, text-to-speech, speech-driven facial animation and image animation as a combined framework to generates a video of the character speaking the generated content with the character's voice. Text data and audio voice lines have been collected and pre-processed to train the text-to-speech model. While pre-trained models are fine-tuned and used for the speech-driven facial animation model and image animation model. To put everything together in an interactive way, a Flask-based Python web application is created to provide as an interface that handles user inputs and generates the result. © 2023 IEEE.

关键词： Animation

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Bengali-English Neural Machine Translation Using Deep Learning Techniques 3

Bengali-English Neural Machine Translation Using Deep Learni...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Paul, Nipun Faruki, Ishmam Pranto, Mutakabbirul Islam Rouf Shawon, Md. Tanvir Mandal, Nibir Chandra Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

Bengali is one of the most widely spoken languages and one of the hardest to translate due to its extensive vocabulary. Earlier, it was fairly difficult to translate from Bengali to English. Using neural machine translation (NMT), it is now possible to translate from Bengali to English quite flawlessly. In order to carry out the task of neural machine translation, four different Seq2Seq models - Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU) - are studied in this work. We combined four distinct datasets and used the resultant dataset in association with the four models. Our study shows that BiLSTM is the most effective model for the Bengali to English NMT task. Here, the resemblance between the generated output and the real one is assessed using two frequently used performance metrics termed BLEU and ROUGE. We have achieved scores of 47.4, 35.8, 32.0 and 22.8 for BLEU-1, 2, 3 and 4 respectively on BiLSTM. Last but not least, our outcomes are among the finest of other studies performed earlier. © 2023 IEEE.

关键词： Neural machine translation

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Automatic Emoji Generation using Inception V3 2

Automatic Emoji Generation using Inception V3

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Himasree, Ambati Kumar, Kada Sushil Meenakshi, M. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347579

Internet and communication technologies are evolving quickly today. As a result, communication is simpler than it always was. This study examines the usage of emojis for real-time emotion recognition. The findings indicate that while there are disparities in the types of emojis used, there are no differences in the frequency of use between genders. Emojis were mentioned as a way for people of both genders to give their communications more depth. Since they make both genders feel at ease when utilizing online communication tools, emojis are also not regarded to be gender specific. The current study focuses on how emojis, which display images of facial expressions, assist people in recognizing emotions. Additionally, it develops the measurement parameters. © 2023 IEEE.

关键词： Deep learning

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Classifying Grape Leaf Disease using an Optimized YOLOv7 Deep Learning Model 4

Classifying Grape Leaf Disease using an Optimized YOLOv7 Dee...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Abraham, John T Hussain, Mohammad Manzoor Srividhya, R. Linda, P Edith Karthik, S. Nareshkumar, R. Bharata Mata College Department of Computer Science Kochi India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Dr G R Damodaran College of Science Department of Computer Science Coimbatore India Kristu Jayanti College Department of Computer Science Bengaluru India SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798331515720

The prevalence of grape leaf diseases has had a profound influence on the worldwide grape industry, resulting in considerable financial setbacks. For effective disease management, it is essential to classify the disease quickly and accurately. This research presents an enhanced YOLOv7 deep-learning model for accurately classifying grape leaf diseases. The dataset is obtained from Kaggle and consists of four categories: black rot, leaf blight, black measles, and healthy. The Adam optimizer is utilized to optimize hyperparameters in order to enhance the performance of the model. The refined YOLOv7 model achieved a remarkable accuracy of 99.24% on the test set, showcasing its potential for detecting grape leaf diseases at an early stage. The model in precision agriculture can aid in the management of grape production, categorization of leaf diseases, and monitoring of operations. © 2024 IEEE.

关键词： Contrastive Learning

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Detection of Colon Cancer Using Inception V3 and Ensembled CNN Model 3

Detection of Colon Cancer Using Inception V3 and Ensembled C...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Swarna, Ishrat Jahan Hashi, Emrana Kabir Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350345360

Colon cancer is one of the most prevalent types of cancer. Early diagnosis of colon cancer can lead to an increased chance of successful treatment with less cost. To speed up this process deep learning can provide very useful and effective approaches. In this thesis work, two types of models were developed to classify colon cells from image data - one is the transfer learning model where a deep network Inception V3 is used as the pre-trained model and the other one is an Ensembled model which combines predictions of three simple sequential CNN models. To develop these models, 10k images were used from the LC25000 dataset and a very small Warwick-QU dataset having only 165 images was used to provide new data for retraining and testing purposes. Both models achieved a high result for the first dataset with 99.4% and 99.95% accuracy respectively, where Inception V3 showed 94.545% accuracy on new data from Warwick-QU after retraining and Ensembled model showed 78.182% accuracy. This approach can be used in research in the field of early and effective detection of colon cancer with a larger amount of varying images and more preprocessing methods to reduce overfitting and to make the model perform well in various types of images. © 2023 IEEE.

关键词： Image classification

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Applying a machine learning model to forecast the risks to children's online privacy and security

Applying a machine learning model to forecast the risks to c...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Rahman Pir, Rumel M.S. Rabbi, Md. Forhad Islam, M. Jahirul Shahjalal University of Science and Technology Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350399387

Children are using digital gadgets at an increasing rate around the world, and this is accompanied by an increase in online privacy and security risks for them. Earlier studies found that children, parents, and teachers can work together to improve children's privacy and security when using digital devices. However, it was also discovered that safety measures can be applied more effectively if it can be predicted earlier which children are at risk and who are not. This research proposes a machine learning model based on 12 data points from children and their parents to predict children at risk of online abuse, cyberbullying, sexting, and stress. The model was created using the interview findings of 42 parents and 30 children as well as separate survey data from 311 parents and 383 children. According to the data study, children suffer the most from abuse, bullying, sexting, and severe mental stress among the various forms of privacy and security violations in the online world and that is why the prediction was done on these 4 situations. In this study, the Random Forest algorithm was used to develop the machine learning model and it was found that the model can somehow predict the privacy and safety of children while using digital devices. Our study contributes to the literature on computer Supported Cooperative Work (CSCW) research that aims to develop cognitive and conceptual models to make the digital environment safer for children and adolescents. © 2023 IEEE.

关键词： Digital devices

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OTP-Based Software-Defined Cloud Architecture for Secure Dynamic Routing

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computers, Materials & Continua 2022年第4期71卷 1035-1049页

作者： Tae Woo Kim Yi Pan Jong Hyuk Park Department of Computer Science and Engineering Seoul National University of Science and Technology(Seoultech)Seoul01811Korea Department of Computer Science Georgia State UniversityAtlanta30303USA

In the current era,anyone can freely access the Internet thanks to the development of information and communication *** cloud is attracting attention due to its ability to meet continuous user demands for ***,Cloud is effective for systems with large data flow such as the Internet of Things(IoT)systems and Smart ***,the use of traditional networking technology in the cloud causes network traffic overload and network security ***,the cloud requires efficient networking technology to solve the existing *** this paper,we propose one-time password-based software-defined cloud architecture for secure dynamic routing to mitigating the above-mention *** proposed cloud architecture provides a secure data path through dynamic routing using One-Time Internet Protocol(OTIP)algorithm between each *** the network side,we use software-defined technology to provide efficient network management and data *** introduce a software-defined cloud architecture that applies OTIP algorithms for secure dynamic *** conduct a comparative analysis between general IP communication and proposed OTIP communication *** evaluates the performance of OTIP ***,we examine the proposed software-defined cloud architecture,including how to apply OTIP in secure dynamic routing according to the results of the comparative analysis.

关键词： Secure routing dynamic routing one-time password softwaredefined network software-defined perimeter cloud computing

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Automatic Nostalgia Detection from Bengali Text 26

Automatic Nostalgia Detection from Bengali Text

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

作者： Debnath, Ameya Karmokar, Bipul Rahman, M. Shahidur Shahjalal University of Science and Technology Department of Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

The rise of user-generated content on digital platforms has led to various types of communication, including comments on social media, articles, and videos. In this context, detecting nostalgic feelings in Bengali comments is a unique challenge. This study focuses on creating a dataset on "Nostalgic Bengali comments"and will check which methods perform better to automatically spot nostalgic Bengali comments. We will use techniques from natural language processing, sentiment analysis, and machine learning to differentiate between comments that contain nostalgia and those that don't. Our dataset includes diverse Bengali comments collected from YouTube. Detecting nostalgic Bengali comments could help us understand user feelings, cultural discussions, and how people engage with content. This research contributes to the field of sentiment analysis by specifically addressing nostalgia in the Bengali language, enhancing our understanding of emotions in digital communication. © 2023 IEEE.

关键词： Bengali Comment Collection from YouTube Bengali Text Analysis Deep Learning Machine Learning Nostalgia Detection

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