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Fuzzy Computing Testing and Study Hydroponic Dosing Systems for Rural Indian Subsistence Farmers Using AI and IoT 4

Fuzzy Computing Testing and Study Hydroponic Dosing Systems ...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Pai, H. Aditya Jyothi, N.T. Lal Jeyan, J.V. Muruga Chandrappa Jain, Prithviraj Arvindhan, M. Lips Research and Dlcard India Lips Research Foundation India Dept. of Computer Science and Engineering Bengaluru India Deportment of Computer Science and Engineering India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

In this study, we look at how well smart hydroponics systems supported by the Internet of Things (IoT) work for small-scale farmers in India's rural areas. The framework undergoes training using Fuzzy Logic artificial intelligence. A fuzzy logic model was created as part of the research carried out in this study. The monitor is connected to the Internet of Things (IoT) collects the data., this framework accurately finds and modifies the output. These instruments are useful in observing the environment variables of the hydroponic setup as well as keeping the fertilizer solution in check. An initial test carried out shows that the real time data generated and collected for testing made up the model evaluation. These are the phases that are undertaken to ensure that the accurate determination of the values are noted for the purpose of changing the environment variables of the hydroponic setup. The study findings describe how the system successfully determines the correct values that are required for the microcontroller., thus confirming that efficacy of Fuzzy Computing and AI used in the hydroponics setup. Here., the emphasis is given to provide accuracy towards the Fuzzy Computing and AI for rural subsistence farmers., especially in the lower to middle income nations. The research also mentions about importance of integrating solar power-enabled system with nearby available Wi-Fi networks., in contrast with the cloud-based IoT platforms so as to optimize the use of Fuzzy Computing - AI in the hydroponics system within the particular environments. © 2024 IEEE.

关键词： Hydroponics

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Recommendation System For Social Platform Using Deep Learning Techniques 8

Recommendation System For Social Platform Using Deep Learnin...

引用

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

作者： Nishchal Narayan, S. SriVidya, M.S. Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and mutual friends, the system aims to provide accurate friend suggestions. This recommendation methodology can be applied across various domains, including search engine recommendations (Google, Explorer, Microsoft Edge), music recommendations (Wynk), video recommendations (YouTube), movie recommendations (Amazon Prime), and product recommendations (Flipkart, Amazon). The study employs similarity coefficient calculations like Jaccard Distance and Cosine Similarity, alongside ranking measures such as PageRank. Furthermore, the system's performance is evaluated using advanced deep learning techniques, including Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Comprehensive experimentation involves measuring the F'l-Score and comparing the accuracy of various machine learning and deep learning algorithms to identify the most effective methods for friend recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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DDOS Detection Using ML and Deep Learning Approaches 8

DDOS Detection Using ML and Deep Learning Approaches

引用

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

作者： Sumukh, S. Sandhya, S. R V College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

DDoS has a huge potential of threat towards the availability and performance of webservices. The attack system makes the systems unavailable to the valid users by flooding them with malignant traffic. Traditional defense mechanisms are mostly ill-designed to counter such fast-changing and increasing attacks, so service disruption, financial loss, and reputational damage are expected results. This work explores the potential of real-time DDoS attack detection and mitigation using machine learning and deep learning, with a particular emphasis on scalability, accuracy, and adaptability. The research involves the creation and assessment of several machine learning (ML) methods, such as Random Forests and Decision Trees, in addition to deep learning (DL) models like Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. The study attempts to determine the best models for real-time DDoS detection by comparing and contrasting these methods. The methodology consists of comprehensive data collecting and preprocessing, model building, training, and evaluation. The performance of the models on parameters such as detection speed, accuracy, and resource efficiency is compared and analyzed. © 2024 IEEE.

关键词： Long short-term memory

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Deep Learning Techniques for Bangladeshi Coin Detection and Automated Counting System: A Comparative Study of Multiple Algorithms 3

Deep Learning Techniques for Bangladeshi Coin Detection and ...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Babor, Ashraf Hussan Choity, Umme Habiba Kaspia, Most. Paul, Subrata Kumer Paul, Rakhi Rani Haque, Md. Momenul Hamid, Md. Ekramul Qadirabad Dayarampur Dept. of Computer Science and Engineering Natore6431 Bangladesh University of Rajshahi Department of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Efficient currency detection and counting systems are crucial for the smooth operation of any financial ecosystem. This paper explores the development, implementation, and potential impact of a specialized currency detection and counting system for Bangladeshi coin currencies. Through this comprehensive study, this paper addresses the unique challenges and opportunities in Bangladesh's coin currency management landscape, highlighting the motivations and results of such a system's introduction. computer vision technology includes currency detection. In this paper, we present a coin currency detection system that can detect coin currency from images. Designing a CNN-based coin recognition system for the recognition of Bangladeshi coins of denominations '1tk', '2tk', and '5tk'. We have created a dataset that contains more than 10,000 images. We photographed coin currencies from both sides, and the system can recognize coin currencies from both sides. For high efficiency, we test on various backgrounds. Experimental results are presented successfully with a training accuracy of 99.99% with VGG16, 99.38% with VGG19, and 99.99% with EfficientNetB0. We also got validation accuracy with VGG16 of 90.90%, 99.96% with VGG19, and 99.51% with EfficientNetB0. © 2024 IEEE.

关键词： Deep learning

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using Image Processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

引用

2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kapoor, Rameshwari Kuliyal, Rashmi Bhanot, Nilesh Bagla, Piyush Rawat, Ruchira Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: Traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70% on the driver distraction dataset. © 2024 IEEE.

关键词： Contrastive Learning

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A Survey on smoking and Alcohol Tracking System: State of the Art, Analysis, and Performance Evaluation 2

A Survey on smoking and Alcohol Tracking System: State of th...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Velavendan, R. Shubram, S. Shyam Sundar, V. Padmavathi, B. Kavitha, D. Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Addiction to alcohol and tobacco is still a major worldwide health issue that requires innovative approaches to early detection and monitoring. This review of the literature provides a thorough summary of recent research projects in the area of tracking systems for alcohol and tobacco use that are both intrusive and non-intrusive for correct classification. The main goal of this research is to investigate how different IOT-based models might improve the precision and effectiveness of alcohol and smoking detection. The measurement of several IoT models used in tracking systems for alcohol and smoking makes up a sizeable section of this work. The study assesses the benefits and drawbacks of each model. Additionally, how effectively it can identify individuals who smoke or drink and how well it can work with various datasets. This research intends to direct future research efforts and encourage the development of more precise and effective tracking systems for smoking and alcohol use, ultimately resulting in improved public health outcomes. © 2024 IEEE.

关键词： Internet of things

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Analyzing and Visualizing Causes of Poverty in India: A Data-Driven Approach Using HPCC Systems 8

Analyzing and Visualizing Causes of Poverty in India: A Data...

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

作者： Skanda, P.R. Shobha, G. Shetty, Jyoti Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Poverty in India is a complex issue with multiple causes. This paper mainly focuses on three of the main causes for poverty: Education, Unemployment and Consumer Price Index. This study makes use of data received from the National Informatics Center (NIC) of India to predict poverty across the various states of India. Through applying preprocessing techniques and employing the HPCC Systems Visualization bundle, the research shows that Bihar is facing a higher level of poverty, whereas Mizoram exhibits converse trend. Subsequently, the HPCC ML-Core bundle is applied to conduct linear regression analysis for predicting poverty scores. This predictive model demonstrated an accuracy of 97%. Lastly, the difference between a single-node and 8-node cluster is presented for runtime performance which clearly shows that the 8-node cluster has an advantage for similar computational tasks and thus makes it suitable. © 2024 IEEE.

关键词： Visualization

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Enhancing Security in Software-Defined Networks: Hybrid Deep Learning Models for Flooding Attack Detection 3

Enhancing Security in Software-Defined Networks: Hybrid Deep...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Mall, Ramya Kumar, Ajay Abhishek, Kumar Kumar, Abhay National Institute of Technology Department of Computer Science and Engineering Bihar Patna India Bennett university Dept of Computer science engineering and technology UP Greater Noida India

ISBN: (纸本)9798331519582

Software-Defined Networking (SDN) enhances flex-ibility and centralizes network traffic management, but it also introduces new security challenges, particularly from flooding attacks such as TCP and UDP floods. These attacks can over-whelm network resources, leading to performance degradation or service disruption. This research presents a novel hybrid deep learning approach for detecting flooding threats in SDN utilizing an imbalanced dataset. Balancing is crucial in imbalanced datasets as it reduces bias towards the majority class, allowing the model to reliably detect attacks from minority classes. We have addressed class imbalance through the application of inverse proportional sampling. Our model employs a hybrid methodology combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to proficiently analyze network traffic and identify malicious flooding activities. The proposed model adeptly tackles the temporal dynamics of flooding attacks by utilizing CNN for feature extraction and LSTM for sequence modeling. This research presents real-time threat detection, enhancing SDN security. Our research indicates that the hybrid deep learning model accurately detects flooding-based denial of service attacks and generalizes effectively across various flooding scenarios, positioning it as a promising solution for the defense of SDN networks. © 2025 IEEE.

关键词： Denial-of-service attack

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