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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

Procedia computer science 2023年 227卷 262-271页

作者： Gregorius Natanael Elwirehardja Teddy Suparyanto Miftakhurrokhmat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Indonesia 55584 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

关键词： Oil Palm Yield Machine Learning XGBoost XAI SHAP

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Leveraging Machine Learning for Next-Generation Cyber Security

Leveraging Machine Learning for Next-Generation Cyber Securi...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Haripriya, V. Sharma, Vishal Dari, Sukhvinder Singh Nivedha, C.S. Tiwaskar, Shweta A. Singh, Akhilendra Pratap Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Mechanical Engineering Jaipur India Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350329773

This paper outlines how machine learning can be used to enhance subsequent-generation cyber safety. We move over how the system gaining knowledge of events may be used to locate unusual behavior or ability threats faster than conventional techniques. We further discuss how the gadget mastering era can research from beyond located styles to better guard towards future security threats. Furthermore, we discuss how device getting to know can be applied to optimize cyber safety approaches with a purpose to make sure the best possible protection for systems. Finally, we talk some ability challenges that industry players should do not forget whilst leveraging gadget gaining knowledge of for subsequentera cyber protection. © 2023 IEEE.

关键词： Cybersecurity

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Exploring the Benefits of DNS-Based Authentication for Wireless Network Security 3

Exploring the Benefits of DNS-Based Authentication for Wirel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swarup, Lakshya Jayadurga, R. Borade, Monali R. Ramachandran, T. Singh, Akhilendra Pratap Prabha, Savita Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Wi-wireless networks are becoming more pervasive and are frequently the primary goal of malicious attackers. Therefore, it is miles crucial that wireless networks are nicely secured to defend their users from malicious moves. DNS-based total authentication is a promising answer for attaining ease of authentication of wi-wireless customers in an open Wi-Fi community. This paper surveys numerous current works that discover the wi-fits wireless of DNS-based total authentication for Wi-Fi wireless community safety. especially this paper opinions three one-of-a-kindwireless strategies of using DNS for authentication purposes: making use of DNS's cache for identifying windows-primarily based Wi-Fi wireless clients;Open Mobile Alliance (OMA)'s tool control (DM) projects to relaxed get right of entry to;and a modern scheme for protecting the use of the DNS-based techniques. Wi-finally, this paper discusses a few safety-demanding situations and future studies directions. © 2023 IEEE.

关键词： authentication initiatives management protecting secured

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Understanding and Developing the Integration of Encapsulating Security Payload in Wireless Network Security

Understanding and Developing the Integration of Encapsulatin...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Haroon, Syed Jangid, Pradeep Kumar Singh, Jagtej Naveena, S. Sathiya Manisha, P. Singh, Vaishali Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India

ISBN: (纸本)9798350329773

The integration of Encapsulating security Payload (ESP) right into a wireless community safety device calls for careful know-how and improvement. ESP is a crucial security protocol which offers an authentication header, an integrity takes a look at, and encryption of payload information. It really works via encapsulating IP datagram's inside sub-protocols with cryptographic functionality, taking into consideration the secure transport of information throughout the network. At some stage in the mixing system, ESP should be configured such that it could offer the desired degree of security at the same time as meeting performance necessities. This typically entails the configuration of suitable cryptographic algorithms, key sizes, and cryptographic mode settings, as well as other parameters. As soon as integrated, ESP ought to be examined to make sure that it meets the desired requirements. This could be done by using evaluating its overall performance in opposition to a variety of attacks, or by means of going for walks a dedicated penetration testing suite. After success integration and trying out, ESP has to be monitored to detect any changes in its protection posture. Additionally, any recognized issues ought to be remediated in a timely manner to decrease the impact of any safety risk. © 2023 IEEE.

关键词： Integration

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Investigating the Effectiveness of Image Restoration Algorithms in Digital Image Processing

Investigating the Effectiveness of Image Restoration Algorit...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sidhu, Amritpal Kantak, Ganesh Kumar Naveena, S. Sathiya Shinde, Gitanjali R. Singh, Vaishali Zaidi, Taskeen Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vivekananda Global University Department of Mechanical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper examines the effectiveness of image healing algorithms for virtual photo processing. The research makes a specialty of different forms of picture recovery algorithms, inclusive of side-retaining methods and regression-based totally methods and their outcomes on photo nice. It proposes a quantitative assessment framework based on a hard and fast of goal metrics for the overall performance of these algorithms. The assessment framework includes simulations to acquire sensible dimension consequences. Outcomes have shown that the regression-based techniques outperformed the brink-keeping techniques in phrases of picture constancy and sharpness. The paper concludes that the proposed quantitative evaluation framework may be used to assess the performance of different picture restoration algorithms in digital picture processing. © 2023 IEEE.

关键词： Image reconstruction

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Long Short-Term Memory (LSTM) Networks for Stock Market Prediction 4

Long Short-Term Memory (LSTM) Networks for Stock Market Pred...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Dhabliya, Dharmesh Al-Jawahry, Hassan M. Sharma, Viklap Jayadurga, R. Jasmin, M. Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Master of Business Administration Chennai India New Prince Shri Bhavani College of Engineering and Technology India

ISBN: (纸本)9798350393248

The amazing capacity of Long Short-Term Memory (LSTM) networks to record complex temporal connections in sequential data has drawn a lot of attention in recent years. In the context of stock market prediction, a field where precise price movement forecasting is crucial, this study investigates the use of LSTM networks. We explore the basic ideas behind LSTM networks and explain how they may be used for time series forecasting because of their ability to analyze and understand sequential data. The first section of the study gives a brief introduction of LSTM architecture and its benefits over more conventional models. We talk about how important feature engineering and data preparation are to improving LSTMs' predictive power for stock market forecasting. We also explore how model assessment methods and hyperparameter adjustment may be used to maximize LSTM performance. In the study, we investigate several approaches, such as single-step and multi-step forecasting, for using LSTM networks in stock market prediction. We look at how external influences affect the performance of the model, including sentiment analysis of news and economic indicators. We also discuss potential remedies to the problems of overfitting and data scarcity that are inherent in LSTM-based stock market prediction. © 2023 IEEE.

关键词： Long short-term memory

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Two-Stream Holographic Convolutional Network for Speech Emotion Recognition Using Korean Audio 4

Two-Stream Holographic Convolutional Network for Speech Emot...

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

作者： Venkateswaran, N. Mudadla, Balaji. Jasmine, R. Rajitha Nalini, J. Rajasekar, M. Jayasudha, T. Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India CMR Technical Campus KandlaKoya Medchal Telangana Hyderabad501401 India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai601206 India Vignan's Institute of Engineering for Women Department of Computer Science Andhra Pradesh Visakhapatnam530049 India Saveetha College of Liberal arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Science And Application Tamil Nadu Chennai602105 India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore641407 India

ISBN: (纸本)9798331529635

Speech Emotion Recognition (SER) has become an important area of research in the AI domain, helping systems comprehend people's feelings with speech, in such areas as healthcare, automated customer support, and virtual companionship. Emotion detection from Korean speech raises learning issues and demand an understanding of all high-frequency spectrum and time selective features. Despite that these layers work effectively, typical convolutional networks may lose phase information which is so important for emotion distinction especially in languages with tone and phonetics. For this research, the AI-Hub Speech Emotional Database, a large scale Korean speech dataset, is employed to create a Two-Stream Holographic Convolutional Network (TSHCN) for SER. However, the TSHCN architecture employed a two-path structure in which one branch encoded the spectral feature and the other branch encoded the temporal dynamics in sequence, while the phase information was retained using holodynamic convolutions for better classification of emotions. In order to increase the efficiency of the model, the Kookaburra Optimization Algorithm (KOA) is used on the parameters of the weight network, reducing the number of mistakes and the load of calculations as well as increasing the accuracy of the model. As more evident from the previously discussed results, SER performance is notably enhanced by the use of TSHCN with KOA;specifically, the accurate representation of Korean speakers' speech emotions will help pave the way for the development of SER for the Korean language and other complex languages as well. This study thus takes up the shortcoming in the conventional approaches to the task and proposes an optimal and language-independent SER model. The introduced approach attains higher accuracy as 99%. © 2024 IEEE.

关键词： Emotion Recognition

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A Novel Method for Sample Selection in Audio Stenographic Systems

A Novel Method for Sample Selection in Audio Stenographic Sy...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Bhargav J. Bhatkalkar R.Vijaya Arjunan Arunaav Alok Ria Sanghavi D. Cenitta Radhika Kamath Department of Computer Science Digipen Institute of Technology Singapore Department of CSE MIT Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9781665482721

This research work proposes a new method for performing sample selection in audio steganographic systems, which can also be applied to strictly frequency-based and spatial steganographic systems. This method exploits the predictability of pseudo-random number generators to generate a sequence of pseudo-random indices to develop a steganographic system that offers security without compromising the payload capacity of the carrier signal. A comparative study has also been performed among existing sample selection algorithms, namely, sequential selection and pseudo-random selection with indices embedded within the stego object. Our analysis also looks at the effect of applying RSA encryption on the source message having the stego object created. For this study, the source messages are both grayscale and RGB image files. The carrier file used is a 32-bit stereo WAV file. The metrics chosen to perform the comparative study include PSNR, Mean Square Error, Normalized Absolute Error, and Structural Content. To judge the resilience of the steganographic systems from a security point of view, the histogram analysis method is utilized to perform steganalysis. Further, the ineffectiveness of encryption in the steganographic process is also analyzed. The proposed method overcomes the vulnerabilities inherent to sequential and random sample selection methods using the unique sample selection method devised in this paper.

关键词： Measurement Histograms Mean square error methods Gray-scale Prediction algorithms Generators Encryption

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches

An Efficient Model for Early Detection of Maize Leaf Disease...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Shanta Kallur Suman Yaligar Puneeth N. Thotad Dept. of Computer Science and Engineering K.L.E.Institute of Technology Hubballi India Affiliated to Visvesvaraya Technological University Belagavi Dept. of Master of Computer Applications K.L.E.Institute of Technology Hubballi India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition.

关键词： Deep learning Training Productivity Adaptation models Technological innovation Accuracy Image recognition Transfer learning Convolutional neural networks Diseases

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Prognosticate Trending Days of Youtube Videos Tags Using K-Nearest Neighbor Algorithm 5

Prognosticate Trending Days of Youtube Videos Tags Using K-N...

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5th International Conference on Information technology for Education and Development, ITED 2022

作者： Olukumoro, S. Olugbenga Adenusi, Cecilia Ajowho Ofoegbunam, Emmanuel Izuchukwu Josephine, Oguns Yetunde Abayomi, Opakunle Victor Yaba College of Technology Department of Computer Technology Lagos State Nigeria Linux Professional Institute Nigeria Master Affiliate UI Ibadan Oyo State Nigeria Federal School of Surveying Department of Computer Science Oyo Oyo State Nigeria The Polytechnic Ibadan Department of Computer Science Ibadan Oyo State Nigeria Federal University of Agriculture Abeokuta Ogun State Nigeria

ISBN: (纸本)9781509064229

YouTube is a video-sharing website where users may publish, watch, share, and comment on videos and other media. The proliferation of technological gadgets, combined with rapid advancements in technology, has resulted in an increase in trending videos on the platform, where videos and content receive hundreds of thousands, if not millions, of views within minutes of being uploaded and continue to trend throughout the day. This study uses the US YouTube Trending dataset, which includes 130591 occurrences and was acquired from the kaggle repository between August 11, 2020 to May 14, 2022. This study used qualitative and quantitative methods to analyze the YouTube videos dataset, and then performed a predictive analysis on the trending video tags, predicting how a particular video on YouTube might trend in the next two to eight days by predicting the trending of such videos for the next two to eight days and showing their accuracy results using the K-nearest neighbor algorithm (KNN). The model that was utilized to perform the prediction analysis has an accuracy of around 98 percent. © 2022 IEEE.

关键词： Forecasting

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