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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Bhatnagar, Shaleen Chadalawada, Archit Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350383652

This study underscores the critical importance of the agricultural sector for ensuring global food security and economic resilience, emphasizing the role of machine learning (ML)-enhanced crop recommendation systems. These systems are increasingly crucial for guiding farmers in choosing crops that are best suited to their unique soil and environmental conditions. Through the analysis of a comprehensive dataset, including soil composition, climate variations, historical crop yields, and agricultural practices, this research evaluates the performance of several ML algorithms-such as neural networks, decision trees, random forests, and support vector machines-in forecasting the most appropriate crops. Training on historical data allows these algorithms to decipher the complex dynamics between environmental factors and crop results. The success of these models is gauged by metrics including accuracy, precision, recall, and F1-score. This study also delves into the models' interpretability, offering crucial insights to both agricultural practitioners and researchers. Moreover, it presents the creation of a user-friendly web application that applies these ML models to provide personalized crop recommendations, requiring user inputs like geographical location, soil characteristics, and weather data. The findings reveal that ML algorithms can significantly empower farmers with knowledge to select suitable crops, thereby boosting agricultural efficiency, optimizing the use of resources, and enhancing the sustainability of agricultural operations. The research highlights the necessity of selecting the right ML algorithms and preprocessing techniques for achieving superior results. Notably, we have enhanced the accuracy of the random forest algorithm to 99.92% and ADABOOST with the Decision Tree Classifier to 99.02%. This contribution to precision agriculture showcases the practical use of ML in crop recommendation systems as a technological approach to addressing agricultural

关键词： Adversarial machine learning

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Real-Time Applicability Analysis of Lightweight Models on Jetson Nano Using TensorFlow-Lite 1st

Real-Time Applicability Analysis of Lightweight Models on J...

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Vidya, Kamath Renuka, A. Vanajakshi, J. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9789819981281

Deep learning models have recently acquired prominence due to their adaptability to constrained devices. Because of this possibility, a significant number of studies in the fields of IoT and Robotics are being done with the goal of deploying deep learning models on resource-constrained applications. A variety of lightweight models are now available that can perform computer vision tasks on constrained devices including the Jetson Nano. However, several enhancements are still needed if this field of research has to prosper in the future. This study was carried out with the aim of comparing and contrasting the lightweight models provided by TensorFlow in order to assess them and ascertain how close they are to practical reality. The conclusions not only present the observed outcomes but also provide insight into the models, attempting to identify potential improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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Development of a Crop Recommendation System Through the Use of Various Machine Learning Algorithms 2

Development of a Crop Recommendation System Through the Use ...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Bhatnagar, Shaleen Lakshmi, Napa Ashwitha, A. Vishnu Srinivasa Murthy, Y. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350328202

The agricultural sector holds immense significance in ensuring global food security and economic stability. Machine learning approaches have garnered increasing attention for agricultural purposes in the past few years, especially when it comes to crop recommendation systems. This research endeavors to introduce and assess various machine learning algorithms deployed in a crop recommendation system, with the primary objective of aiding farmers in making well-informed decisions regarding crop selection, considering their unique environmental and soil conditions. The study relies on a comprehensive data set encompassing vital elements including soil composition, climatic trends, past crop yields, and agronomic techniques. Predictive models are generated by utilizing a variety of machine learning algorithms, such as neural networks, decision trees, random forests, and support vector machines. To understand the complex interactions between environmental factors and crop performance, these models are trained using historical data. To assess how well these models work in terms of suggesting appropriate crops under circumstances, performance metrics including accuracy, precision, recall, and F1-score are used in the evaluation process. Moreover, the research delves into the interpretability of these models to offer insights into the decision-making process, catering to both farmers and agricultural experts. Furthermore, the study discusses the practical implementation of these models into a user-friendly web application, thus enhancing accessibility for farmers. This application solicits input from users, including geographical location, soil attributes, and climate data, subsequently generating personalized crop recommendations based on the insights gleaned from machine learning models. The findings of this study highlight how machine learning algorithms can greatly assist farmers in choosing crops that are specific to their needs. Such help may increase agricultural prod

关键词： Crops

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The Future of Online Shopping: Blockchain- Powered E-Commerce Platforms

The Future of Online Shopping: Blockchain- Powered E-Commerc...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sri, Nemani Divya Ayyasamy, Ramesh Kumar Chinnasamy, P. Vellanki, Sreelakshmi Vujwala, Maddula MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798331540685

A cutting-edge online marketplace that uses blockchain technology to transform how we purchase and sell goods and services is known as a blockchain- powered e-commerce platform. This platform uses a decentralized network of computers, also known as nodes, to maintain a transparent and impenetrable record of all transactions instead of depending on a central authority like a bank or a company. A blockchain-based e-commerce platform is a secure, transparent, and trusted online marketplace. It does away with the need for intermediaries, improves security, and provides a world market where users can confidently conduct peer-to-peer transactions. This novel strategy could change how online commerce and shopping are performed. © 2024 IEEE.

关键词： Marketplaces

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Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 824-835页

作者： Min Yang Zhenwei Wang Qiancheng Xu Chengming Li Ruifeng Xu Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenChina School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaUSA School of Intelligent Systems Engineering Sun Yat‐Sen UniversityGuangzhouChina School of Computer Science Harbin Institute of Technology(Shenzhen)ShenzhenChina

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable ***,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic *** this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training *** learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation *** experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual *** reproducibility,we release the code and data publicly at:https://***/siat‐nlp/HSEMEC‐code‐data.

关键词： deep learning emotional conversation generation semantic‐emotional memory

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Intrusion Detection System Using Machine Learning

Intrusion Detection System Using Machine Learning

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kiran, Ajmeera Prakash, S. Wilson Kumar, B Anand Likhitha Sameeratmaja, Tammana Charan, Ungarala Satya Surya Ram MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India

ISBN: (纸本)9798350348217

The use of computers and the internet has spread rapidly over the course of the past few decades. Every day, more and more people are coming to rely heavily on the internet. When it comes to the field of information security, the subject of security is one that is becoming an increasingly important focus. It is vital to design a powerful intrusion detection system in order to prevent computer hackers and other intruders from effectively getting into computer networks or systems. This can be accomplished by: (IDS). The danger and attack detection capabilities of the computer system are built into the intrusion detection system. Abuse has occurred and can be used to identify invasions when there is a deviation between a preset pattern of intrusion and an observed pattern of intrusion. An intrusion detection system (IDS) is a piece of hardware (or software) that is used to generate reports for a Management Station as well as monitor network and/or system activities for unethical behaviour or policy violations. In the current study, an approach known as machine learning is suggested as a possible paradigm for the development of a network intrusion detection system. The results of the experiment show that the strategy that was suggested improves the capability of intrusion detection. © 2023 IEEE.

关键词： Support vector machines

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Early Forest-Fire Detection by Linear Regression, Ridge Regression and Lasso Regression 1

Early Forest-Fire Detection by Linear Regression, Ridge Regr...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Mandre, Ananya. Hebbar, Deeksha.R. Rao, J. Shreya Keshav, Ananya Kamal, Shoaib Rao, Trupthi Global Academy of Technology Department of Computer Science Engineering Bangalore India Global Academy of Technology Department of Electronics and Communication Engineering Bangalore India Global Academy of Technology Department of Information Science Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Science Bangalore India

ISBN: (纸本)9798350339727

Forest fires are the wildfires that grow uncontrollably, burning plants, animals, grassland and bushlands. For reducing the losses caused by the disaster an effective fire detection device is required. The main agenda of This research will provide a model for early detection. of forest fire. Previously many methods were proposed for the detection such as Neural network model, Three dimension otsu method, NMDI methodology and many more, As we can see there are certain limitations for all these methods. We have implemented certain methodology such as Ridge regression, Lasso regression and Linear regression, equations and the methodology used in this system is studied and implemented during this study. Thus all these three systems demonstrate the required solution for the problem with a good percentage of accuracy, © 2023 IEEE.

关键词： Fires

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Optimized Crop Prediction and Monitoring Using Ensemble Machine Learning Algorithms 2

Optimized Crop Prediction and Monitoring Using Ensemble Mach...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavitha, H.S. Usha, S.M. Sheela, S.N. Anu, H. Sharmila, N. Mallikarjunaswamy, S. Basavaraju, N.M. Ravi, P. Pavithra, G.S. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Dept of Computer Science and Engineering Mysuru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper addresses the urgent need for enhanced agricultural practices by pinpointing the significant limitations of existing crop prediction and monitoring systems. Traditional methods, often characterized by low accuracy and high resource consumption, severely limit effective farming decision-making and sustainability. In response to these challenges. A proposed method is introducing the Optimized Crop Prediction and Monitoring Model (OCPMM), a groundbreaking approach that harnesses the power of advanced Machine Learning Algorithms (MLAs) to elevate accuracy and efficiency in crop management. The OCPMM distinguishes itself through innovative features such as real-time data analysis and predictive analytics, establishing a new benchmark for precision agriculture. Our comparative analysis reveals that the OCPMM markedly surpasses conventional methods, such as the Standard Crop Yield Estimation Technique (SCYET), across key metrics, including prediction accuracy, resource utilization, and time efficiency. These enhancements are instrumental in optimizing crop yields, minimizing waste, and advancing sustainable agricultural practices. By rigorously addressing the deficiencies of traditional systems and highlighting the superiority of the OCPMM with empirical evidence, this study illuminates the transformative potential of machine learning technologies in agriculture. It presents a scalable, reliable, and efficient framework poised to revolutionize agricultural practices, paving the way for a future where farming decisions are informed by precision and foresight. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 59-71页

作者： Praveenchandar, J. Kumar, S. Vinoth Paul, A. Christopher Mukunthan, M.A. Maharajan, K. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Information Technology Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings. © 2023 by the authors of this article. Published under CC-BY.

关键词： Crime

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Assessment of ridge regression-based machine learning model for the prediction of automotive sales based on the customer requirements

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Interactions 2024年第1期245卷 1-13页

作者： Akash, C. Renga Vivekanandhan, P.K. Adam Khan, M. Ebenezer, G. Vinoth, K. Prithivirajan, J. Kishan, V. J. Pranesh Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India

The primary objective of the paper is to predict the price of the "Toyota Cars" based on the requirement of the customer. The primary need is to identify the minimum price of the "Toyota Cars" based on the 37 key attributes/factors. The levels of each factor in the data set varies from 2 to 312, moreover the data sets used for the analysis, contain both feature as well as numerical based data. The data sets are taken from the "KAGGLE" an open-source real time data provider. The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of the "Toyota Cars", thereby identifying the minimum price based on different combinations of attributes. The prime novelty lies on the usage of the Ridge regression, a linear regression algorithm for the prediction process. The ridge regression model was developed by means of the Python programming and Google Colab, an open-source online compiler for the ML was used for the code compilation process. Based on the result, the minimum price was found to be 6520.8 dollars, with the basic customer requirements such as Model: Corolla 1.6-16v, Type: Sedan, 4 Doors, CNG, 69hp, 1854 cc, with air conditioner, centre lock and power steering. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Sales

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