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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Saravanan, S. Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs. © 2023 IEEE.

关键词： Network performance

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Deep Neural Network-Based Approach to Identify the Paddy Leaf Disease using ResNet50-V2

Deep Neural Network-Based Approach to Identify the Paddy Lea...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Islam, Md. Ashiqul Rahman, Md. Mizanur Shohan, Abdul Alim Pulok, Rishad Amin Akter, Sanzida Ahmed, Md. Tanvir Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh East-West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350313413

South Asian countries especially Bangladesh, India, and Srilanka are very involved in paddy cultivation. In Bangladesh and India, one of the key producing crops is paddy. Worldwide more than 40 percent of the world's population derives most of its calories from rice and traditionally farmers are directly involved production of rice. But every year lot of farmers have to face a tremendous amount of loss due to paddy leaf disease. Major of the farmers do not know how to identify perfectly in the primary stage that's why they are facing loss. Due to the increase in population, it is very important to make paddy cultivation scientific and produce more. There are more than 30 types of paddy leaf disease and from there in Bangladesh, approximately 10 types of disease are quite similar. If it is possible to ensure the leaf disease in the very primary stage within a very short time, then every farmer can reduce their loss. Nowadays many researchers are interested in identifying paddy leaf disease using modern science. In this paper, we worked on paddy leaf disease detection and it has worked on 4 types of disease and 1 healthy and without any disease leaf class of paddy. This paper through an automated detection approach using deep learning techniques achieved the highest accuracy instead of the previous applicable working model and traditional manual disease detection process. In this paper, we have analyzed it using 4 different types of models. ResNet50-V2, Inception-V3, MobileNet-V2, and DensNet-201 algorithm models have been applied here and the finding of the research is, that all the models achieved more than 95% accuracy from these ResNet50-V2 has achieved the best accuracy and it is 99.06%. We hope that these deep learning models and techniques will detect the disease with high accuracy in the very beginning time and it will help reduce damage and loss of paddy cultivation. © 2023 IEEE.

关键词： Deep Neural Network DenseNet-201 MobileNet-V2 ResNet50-V2

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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms 4

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vignesh, Puthucode Ganesh Nithin Kumar, S. Senthil Kumar, T. Jeyakumar, G. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331523923

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments. © 2025 IEEE.

关键词： Fall detection

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EEG-Based Alzheimer's Disease Detection Using Deep Learning Techniques 12

EEG-Based Alzheimer's Disease Detection Using Deep Learning ...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Ancy, S.A. Panachakel, Jerrin Thomas College of Engineering Dept. of Computer Science and Engineering Trivandrum India College of Engineering Dept. of Electronics and Comm. Engg. Trivandrum India

ISBN: (纸本)9798350340204

Alzheimer's disease (AD) is a neurological condition characterized by the degeneration of neuron cells, leading to cognitive impairment. This project investigates the effectiveness of EEG-based features for detecting Alzheimer's Disease using deep learning techniques. The dataset comprises EEG recordings from healthy controls, Frontotemporal Dementia (FTD) patients, and patients diagnosed with AD. Data preprocessing involved segmenting the EEG signals into fixed-length epochs and filtering the baseline EEG into alpha, beta, and theta frequency bands. Features such as Mean Phase Coherence (MPC), Differential Entropy (DE), and Mean Squared Coherence (MSC) were computed pairwise for each epoch and frequency band. A feature matrix was constructed for each subject by stacking these features, which were then converted into images and used as input data for a ResNet50-based pre-trained model. The model was tested in leave-one-out cross-validation setting and achieved results comparable to the state-of-the-art in distinguishing between EEG recordings from healthy and AD-affected subjects. © 2024 IEEE.

关键词： Electroencephalography

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing 26

Movie Genre Prediction Using AI-Generated Data and Natural L...

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

作者： Tanvir, Sifat Abontee, Ayesha Parpia Rayhan, Md. Abu Rahman, Md. Fuadur Sultana, Tasnim Ahmed, Ajwa Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved. © 2023 IEEE.

关键词： Embedding GPT-3.5 Neural Network NLP Text Vectorization

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Distinguish between Obese and Normal Body Types Through Gait Analysis Using Classification Models 6

Distinguish between Obese and Normal Body Types Through Gait...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yi, Jae-Sik Han, Ji Hun Kim, Min Jeong Hong, Youn-Sik Incheon National University Dept. of Computer Science and Engineering Incheon Korea Republic of

ISBN: (纸本)9798350344349

For gait analysis, an IMU sensor was mounted on the knee and gait related data was collected. Various gait parameters such as gait time, stance swing ratio, heel strike, and toe off can be extracted from the dataset. To explore the relationship between gait parameters and individual gait characteristics, we analyzed the gait patterns of normal and obese people were analyzed based on BMI (Body Mass Index). To apply it to a classification model of machine learning, different gait cycles between subjects were normalized. Gait data was collected from eight subjects in their 20s. Using this dataset, we applied a logistic regression model, and obtained the classification accuracy of 92%. We also investigated the correlation between BMI and gait parameters and found that, the correlation between BMI and cadence was-0.66. © 2024 IEEE.

关键词： Gait analysis

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning 2

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Skandarsini, R.R. Sangavi, M.S. Harika, Ravitla Varun, C.M. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798350365269

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact. © 2024 IEEE.

关键词： Predictive analytics

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Ensemble Approaches for Offensive Language Detection in Kannada 3

Ensemble Approaches for Offensive Language Detection in Kann...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Monish, M.N. Sooda, Kavitha B.M.S. College of Engineering Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

In the realm of natural language processing, sentiment analysis remains a focal point due to its pivotal role in discerning and analyzing public sentiments. While past research has been dedicated to enhancing the performance of sentiment analysis models, this study delves into the potential benefits of ensemble techniques, with a special emphasis on Genetic Algorithm (GA) optimization, The main thrust of this research was to investigate whether merging multiple models and subsequently optimizing their weights via GA could elevate performance. Evaluations were thoughtfully conducted, focusing on the weighted precision, recall, and F1-scores of individual models. Additionally, the study compared the ensemble methods, both with and without GA optimization, The ensemble method, even without the GA optimization, secured an impressive F1-score of 0.73. Upon integrating the GA optimization, there was a gentle improvement in the ensemble's performance, achieving an F1-score of 0.75, In summing up, this study manifests a palpable enhancement in sentiment analysis outcomes when shifting from standalone models to a GA-optimized ensemble mechanism. The results, epitomized by an F1-score peaking at 0.75, underline the intrinsic merits of harnessing GA for weight optimization in ensemble constructs. © 2024 IEEE.

关键词： Genetic algorithms

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Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach

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Journal of Mobile Multimedia 2023年第2期19卷 363-388页

作者： Kumar, Sunil Kumar, Dilip Dept. of Computer Science and Engineering National Institute of Technology Jamshedpur India

Internet has revolutionized the world in a way no one could have ever imagined. It paved the way for various different technologies, that have transformed the world exceptionally. Internet enabled cloud technology which provides cost-effective, scalable, on-demand computing resources with little to no downtime. Cloud storage allows its users to store and access private data from anywhere in the world without needing any high-end computing system. Cloud storage isn’t always secure, but that doesn’t imply it isn’t. The security of a data depends on the security policies followed by the provider along with the security of the communication channel via which the data is being sent. Encryption is used to obfuscate the data so that it can only be viewed when correct credentials, known as encryption keys, are provided. Following study proposes an encryption technique using ECC (Elliptic Curve Cryptography) along with AES (Advance Encryption Standard) to provide data confidentiality in an efficient way for securing data on cloud and hence, protect the personal information of user from any adversary. This new method is more effective, and the results are superior as a consequence. © 2022 River Publishers.

关键词： AES asymmetric encryption authentication blowfish cloud storage security ECC Symmetric encryption

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Facial Emotional Recognition Using Convolutional Neural Network 2

Facial Emotional Recognition Using Convolutional Neural Netw...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Raj, Deepak Wassay, Md. Abdul Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798350320923

The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques to support his theory like giving shocks of electric impulses to understand how a person reacts to muscular contractions. He also tried to induce some of the expressions of bizarre looking. The ultimate aim is to analyze how much muscles contribute to emotion. He successfully derived lots of human emotions which is hidden. After 200 years this field is still active as it requires more experiments to extract invisible truths of human truths. Lots of Emotion Recognition which is automatic have been seen in the field of Marketing and Advertising. It is also seen in the field of Medical, Law and order...etc. The main fundamental question arrives whether it is good to enter into our personal space. This a question which has lots of dimension. Those who are against this practice is claiming that this is a violation of human rights by which we can use the stored data to harm the society in future. Even though it is a major concern if we can rectify the above the scope in this area is phenomenal. Lots of researchers are still working on this area because of mainly one reason that is its wide application in the area of Medical science. It can be used by a doctor for diagnosing a person with certain psychological disorders, children with Autism, person with Parkinson's disease. It can be widely used to diagnose children with Autism so that particular child can be motivated at early stage leads to his success in future and thus provide a good citizen to the society. In our research we are using several datasets such as FER2013, CK+...etc. These datasets have been given to a model which includes Deep Convolutional Neural Network. Here input image is given to a model using camera. The particular image has to done preprocessing to extract fine features in the model. A

关键词： Emotion Recognition

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