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

DigiFarmer: Precision Agriculture Empowered by Machine Learn...

引用

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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Detection of Airports and Runways From Aerial Imagery Using Deep Learning 8

Detection of Airports and Runways From Aerial Imagery Using ...

引用

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

作者： Vasavi, S. Paul, Valeti Abhishek Abhilash, Vandanala Gnaneswar, Kotha Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Disaster management calls for as quick a detection of critical infrastructure-like airports and runways. Most of the traditional object detection models, which include CNNs, R-CNN s, SSDs, and earlier versions of YOLO, have high computational costs and low accuracy, particularly for smaller objects within aerial and satellite imagery. It presents the YOLOv9 object detection model with very significant improvements in feature extraction, bounding box prediction, and several modern techniques involving attention mechanisms and multi-scale feature fusion. For the purpose of improving model robustness, extensive data augmentation was adopted in training. The proposed model was tested and validated on the diverse dataset of aerial and satellite images, attaining an accuracy as high as 91.3% with a low false alarm rate of just 7.08%. These further improvements will make YOLOv9 highly scalable to support disaster management with faster infrastructure assessments, improvement in border surveillance, among others. © 2024 IEEE.

关键词： Disaster prevention

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Leveraging ReRAM Sequence Graphs for Efficient Mapping of Binary Adders in ReRAM Crossbars 28

Leveraging ReRAM Sequence Graphs for Efficient Mapping of Bi...

引用

28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Dhal, Pragnya Deb, Arighna Das, Subrata Das, Debesh Kumar Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Supreme Knowledge Foundation Group of Institutions Dept. of Computer Science & Engineering Hooghly India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350380101

Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles. © 2024 IEEE.

关键词： data transfer

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An Architecture for Dynamic Load Balancing in Cloud Environment 2

An Architecture for Dynamic Load Balancing in Cloud Environm...

引用

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

作者： Lokesh, Gudivada Baseer, K.K. Dept. of Computer Science and Engineering Tirupati India Department of Data Science Tirupati India

ISBN: (纸本)9798350347579

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future. © 2023 IEEE.

关键词： Energy utilization

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

引用

23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Cardiomegaly Detection Using Deep Learning 4

Cardiomegaly Detection Using Deep Learning

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Suganthi, S.U. Sivabalan, A. Akash, M.V. Barath, B. Goushik, G. Anto Akash, P. Sri Sairam Institute of Technology Dept. of Artificial Intelligence And Data Science Tamil Nadu Chennai India Sri Sairam Engineering College Dept. of Artificial Intelligence And Data Science Tamil Nadu Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This research introduces an innovative method for forecasting cardiomegaly, a common heart condition marked by an enlarged heart, by combining deep learning and machine learning methods. Using the ResNet50 architecture, we examine chest X-ray images to identify cardiomegaly indications. At the same time, a Random Forest algorithm analyses clinical data in a table format, such as age, gender, and different cardiac indicators, to improve prediction accuracy. Our approach combines these models to use unstructured images and structured health data, resulting in a comprehensive diagnostic tool. The data, collected from public health databases, was carefully labelled to guarantee accurate training and validation. Preliminary results showed the promising accuracy of the hybrid model, surpassing individual approaches, and demonstrating the potential of integrated models to improve diagnostic capabilities in cardiology. This study helps improve medical imaging and predictive analytics in healthcare, providing a productive solution for early detection and management of cardiomegaly. The results highlight the promise of combining AI models to change diagnostic methods and enhance patient results. © 2024 IEEE.

关键词： Deep learning

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A Survey on Fish Size Measurement in Aquatic Environments

A Survey on Fish Size Measurement in Aquatic Environments

引用

2024 IEEE Bangalore Humanitarian Technology Conference, B-HTC 2024

作者： Ankitha, K. Venugopala, P.S. Kumaki, Takeshi NMAMIT Dept. of Artificial Intelligence and Data Science Nitte India Ritsumeikan University Dept. of Electronic and Computer Engineering Kusatsu Shiga Japan

ISBN: (纸本)9798350344509

Fish size measurement in aquatic environments plays a pivotal role in understanding fish biology, ecology, and fisheries management. This survey explores various methodologies employed for fish size measurement, emphasizing their significance in aquaculture, fisheries, Ocean and ecological research. Techniques encompass both contact and non-contact methods, with advancements in imaging technologies allowing for non-invasive measurements. These approaches involve the use of cameras, image processing software, and advanced machinery to estimate fish size without causing stress or harm to the fish. While these methods offer promising avenues for precise measurements, challenges exist, including accuracy concerns and environmental limitations. Consequently, there's ongoing research to refine these techniques and ensure their applicability across different fish species and aquatic conditions. Overall, this survey highlights the evolving background of fish size measurement techniques and their importance in various domains within aquatic sciences. © 2024 IEEE.

关键词： Fish

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

引用

2024 International Conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Mewada, Dhruv Gaikwad, Shlok Gharat, Bhavika Kamble, Priyanshu Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence&Data Science Mumbai India

ISBN: (纸本)9798350359688

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness. © 2024 IEEE.

关键词： online tests proctoring remote learning

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Boothless Toll Collection System 4

Boothless Toll Collection System

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sarkar, Swagata Selvabharathi, M. Sarveswaran, D. Arun Raja, M. Raju, Aswin Sri Sairam Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

As a developing country, it is critical that we prioritize the construction and improvement of our road infrastructure in order to stimulate economic growth and promote easier logistics operations. However, the current toll collection system is hampered by difficulties such as overcharging and traffic congestion, reducing its effectiveness. To solve these issues, we propose implementing an advanced toll collection system that includes GPS technology, an ANPR camera, and a variety of machine learning algorithms. This new method will transform toll collecting by properly calculating toll charges based on the actual distance traveled by vehicles on the highway, removing the need for drivers to stop at toll booths. The system uses GPS technology to precisely compute the distance traveled by each car, while ANPR cameras guarantee the journey's validity. As a result, vehicles will only be paid for what distance they have traveled, reducing overcharging concerns and congestion at toll gates. Our major goal is to create a smooth and efficient tolling mechanism that overcomes the limitations of traditional tollgate systems. We hope to improve the toll collection process, shorten wait times, and reduce the consumption of fuel associated with long lines at toll booths by utilizing cutting-edge technologies. By applying this innovative strategy, we hope to improve not only toll collecting efficiency but also commuters' overall driving experience. © 2024 IEEE.

关键词： Traffic congestion

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Optimization of Rainfall Prediction Using Satellite data Through Machine Learning and Deep Learning Algorithms

Optimization of Rainfall Prediction Using Satellite Data Thr...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Ramani, Kasarapu Reddy, Maddireddy Srilekha Bhavani, Kelavathi Feeza, Syed Bavesh, Vellampalli Sai Dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College Department of Csse Tirupati India

ISBN: (纸本)9798350382693

In recent years, non-periodic rainfall has increased globally. The importance of accurate rainfall prediction model is increased in order to reduce the losses due to floods and drought. Rainfall prediction plays a crucial role in decision-making, flood and drought risk assessment and management. In agriculture, it guides many farmers in optimizing crop planting schedules and irrigation. It ensures better yields and reduces losses from droughts and floods. Accurate rainfall prediction benefits us in effective water resource management and energy sector planning. In India, the rainfall varies rapidly due to the vast geography. There are many factors which effects rainfall like distance from the sea, seasons, the presence of mountains, prevailing winds and precipitation. This research work mainly focuses on developing an efficient ensemble-based Machine Learning Techniques such as Extreme Gradient boosting algorithm (XG Boost) and Logistic Regression (LR) for predicting rainfall in different regions of India using satellite data and comparing results with the deep learning algorithms such as Convolutional Neural Network (CNN), Multiple Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Radial Basis Function Network (RBF). The aim of this paper is to overcome the drawbacks of existing systems and build improved prediction model with accuracy more than 90% in order to enhance water resource management, early warning systems, improved urban planning, agriculture, and climate change studies. © 2024 IEEE.

关键词： Radial basis function networks

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