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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model 2

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

引用

2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Putra, Dody Ichwana Ekariani, Shelvi Universitas Andalas Dept. of Computer Engineering Padang Indonesia Universitas Andalas Dept. of Mathematics and Data Science Padang Indonesia

ISBN: (纸本)9798350389241

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26-28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems. © 2024 IEEE.

关键词： Wi-Fi

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Estimating Surface Soil Moisture based on Remote Sensing data and Crop Yield Classification using Machine Learning 9

Estimating Surface Soil Moisture based on Remote Sensing Dat...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Anuradha, G. Susrith, Maddi Siddhardha, Kondreddy Gnana Velagapudi Ramakrishna Siddhartha Engineering College Dept. of computer science and engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350377972

Soil moisture plays a critical role in environmental monitoring and sustainable agricultural practices due to its significant impact on crop growth, water availability, and overall ecosystem health. Properly understanding and managing soil moisture is vital for efficient water usage, especially in agriculture. Traditional methods in measuring soil moisture have been confined to ground-based small-scale methods, which are high price due to high labor intensity and limited spatial coverage. This project addresses its limitation in the spatial view by integrating remote sensing data from the Sentinel-1 satellite. Sentinel-1 is particularly effective in monitoring the soil moisture as it captures data in all climate conditions. By combining the satellite data with Google Earth Engine (GEE) we create a soil moisture dataset. For analyzing the joint satellite and ground truth data, the machine learning technique using Random Forest (RF) is applied and classification of crop yield is done. © 2024 IEEE.

关键词： Soil moisture

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine Learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Education for Visually Impaired Students Using Augmented Reality 5

Revolutionizing Education for Visually Impaired Students Usi...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Janapati, Malathi Naveen, Budamcharla Venkata Sanuth Reddy, Mettu Sai Dinesh, Balabomma Sai Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331530013

This research explores the use of Augmented Reality (AR) to enhance learning experiences for visually impaired students. By utilizing an Android application, the system captures images of classroom boards and processes them using the EasyOCR library for text extraction. The extracted text is then rendered in 3D using Unity, allowing visually impaired students to interact with the content more effectively. The AR experience enables students to zoom in on the text, adjust font sizes, and explore the information at their own pace. This innovative approach integrates the power of AR to overcome the challenges faced by visually impaired students in traditional classroom settings, promoting inclusivity and enhancing their learning experience. © 2024 IEEE.

关键词： Students

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Enhanced Deep Learning-Based Malware Classification Using MobileNetV2 4

Enhanced Deep Learning-Based Malware Classification Using Mo...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Manasa, V.Gnana Sri Gopinath, M. Ram, M.Aditya Khan, Md.Sajid Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Kanuru India

ISBN: (纸本)9798331529635

In the rapidly evolving landscape of cybersecurity, the classification of malware has become increasingly critical due to the exponential growth in the volume of malware, which poses significant security threats to financial institutions, businesses, and individuals. Traditional signature-based detection methods have struggled to keep up with the growing variety and complexity of new threats, making it necessary to use more advanced techniques. Among these, deep learning has emerged as a promising approach for malware detection, particularly through the classification of malware images by transforming binary files into grayscale representations. While previous research has predominantly utilized deep learning architectures for this task. However, in this research work, we developed a model with Mobilenet to enhance classification accuracy. The results of this research demonstrate the revolutionary potential of deep learning-based methods for malware family classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Graph-based Named Entity Information Retrieval from News Articles using Neo4j 18

Graph-based Named Entity Information Retrieval from News Art...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chaudhary, Shikha Vyas, Hirenkumar Arora, Naveen D'Mello, Sejal Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

The internet, social media, and other cutting-edge technologies are all contributing to the exponential growth of digital information. This makes interpreting and applying this enormous amount of data both possible and challenging. Our research proposes a smart system that can convert unprocessed text into a knowledge graph in order to address this. This system makes use of the graph database Neo4j. Although there are currently available tools for linking entities and models to extract relationships, such as Spacy, NLTK, and Flair, their combined use is inefficient. Our suggested method seeks to efficiently blend entity linkage and relation extraction, allowing us to convert raw data into a knowledge graph. There are many real-world uses for this strategy, particularly in data analysis and decision-making. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cypher Queries Graph database Information Retrieval Named Entity Recognition Neo4j News Articles Relation extraction

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Military Aircraft Detection from Optical Satellite Images Using Convolutional Neural Networks 8

Military Aircraft Detection from Optical Satellite Images Us...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vasavi, S. Kalahasti, Tejaswini Kotturu, Manvitha Sri Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Aircraft detection using optical images plays a vital role in various military operations contributing to security, safety, efficiency, and environmental sustainability in the aviation sector. Recent works such as CGC-net, YOLOv5, DenseNet have showed remarkable outcomes in military aircraft detection. Although the models demonstrate significant results, they often exhibit low speed and unreliable accuracy in detecting aircrafts from foggy optical images. To address these issues, we plan on developing a deep learning model that detects military aircrafts with high speed and accuracy particularly in challenging weather conditions. This paper proposes an enhanced CNN model that has been well trained with MAR20 dataset, with various pre-processing techniques applied. The trained model has consistently shown reliable results when tested with unseen data. This model was validated and it yielded an accuracy of 92.35 % and over all false positive rate is 0.41 %, indicating that it performs well in a variety of landscapes and circumstances, with an additional advantage of improved border surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting Suspicious Military Tents from Optical Images using Deep Learning Model 3

Detecting Suspicious Military Tents from Optical Images usin...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vasavi, S. Praneeth Polu, Venkata Sai Manikya Venkata Saketh Nandula, Bala Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350364569

Locating suspicious military tents or encampments is crucial for national security. Recently, deep learning methods have become increasingly popular for detecting military objects at country borders. Traditional methods, which rely on manual checks and morphological algorithms, are often inaccurate and slow at detecting such unauthorized activities. To overcome this issue, a solution that automatically detects suspicious tents from satellite images using deep learning techniques is crucial. An enhanced YOLOv8x-obb model is proposed and trained with a satellite image dataset that is well-prepared with various preprocessing and augmentation techniques. This model was validated and produced an accuracy of 91.9% and false alarm rate of 6.08%, specifying that the model works well in different terrains and situations, and it has the advantage of better border surveillance. © 2024 IEEE.

关键词： Military satellites

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Retroflex: Uncovering Visual Equivalences through Reverse Image Recon 18

Retroflex: Uncovering Visual Equivalences through Reverse Im...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Yadav, Sneha M. Joshi, Karik Shailendra Mandavia, Divyah Tarun Puri, Parth Mahendra Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

This research introduces a comprehensive method for Reverse Image Search (RIS) this can be also called as CBIR that is Content Based Image Retrieval designed for large datasets with more than 50,000 photos in 25 different classes. The approach uses sophisticated deep learning feature extraction methods, such as Auto-encoders and CNNs, to extract complex visual representations for reliable content characterization. We incorporate advanced indexing techniques to address the issues of scalability and retrieval efficiency. The resultant framework exhibits higher accuracy and speed and provides an optimal and sophisticated solution for picture retrieval problems. This effort is a major step forward in improving the capabilities of RIS systems, offering a flexible tool for real-world applications that need for thorough and effective image retrieval across a range of classes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Auto-encoders CNN Content Based Image Retrieval (CBIR) datasets Framework Reverse Image Search (RIS) Sophisticated Deep Learning Feature Extraction

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DigiFarmer: Precision Agriculture Empowered by Machine Learning for Sustainable Crop Management 2

DigiFarmer: Precision Agriculture Empowered by Machine Learn...

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

作者： Patra, Karan Devanand Adesh Bhosle, Arya Kothari, Deepali Mukesh Nagarhalli, Tatwadarshi P. Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350365269

In agriculture, the sector grapples with pressing challenges arising from climate change, water shortages, and pest infestations. Particularly affecting farmers in developing nations, these challenges amplify issues of food insecurity and poverty, necessitating innovative solutions for sustainable global agricultural progress. Addressing the complexities of climate variability, water scarcity, and pest outbreaks requires comprehensive tools and information. The absence of predictive insights intensifies crop losses and hampers overall profitability. In response, this research introduces DigiFarmer, an innovative solution employing advanced machine learning models. DigiFarmer adopts a holistic approach, encompassing crop disease detection, weed classification using Convolutional Neural Networks (CNNs), and crop quality analysis with a Light Gradient Boosting Machine (LGBM) classifier. Yield prediction is facilitated by the Random Forest algorithm, with real-time weather data integration enhancing efficacy. DigiFarmer stands out for empowering farmers with tools for sustainable and resilient agriculture. Leveraging cutting-edge technologies, DigiFarmer not only addresses immediate challenges but equips farmers with foresight to mitigate future risks. The ultimate goal is to contribute to the welfare of global farming communities by enabling informed decisions that enhance productivity, profitability, and sustainability in agriculture. © 2024 IEEE.

关键词： Profitability

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