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A New Approach of Machine Learning And Deep Learning Algorithms Based Crop Yield Prediction

A New Approach of Machine Learning And Deep Learning Algorit...

2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prajapati, Yogendra Narayan Tomar, Swati Singh, Pancham Gupta, Anurag Kumar, Varun Arora, Neerja Ajay Kumar Garg Engineering College Computer Science & Engineering Ghaziabad India Ajay Kumar Garg Engineering College Information Technology Ghaziabad India Kiet Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350314946

The science and art of raising plants and wild animals is called agriculture. India's agriculture ranks second in the world and its agricultural areas make up 60.45% of the country's land. The Indian economy mainly supports the agriculture and agro industrial sectors. Crop rotation, soil consistency, weather and temperature, precipitation, and other factors all affect crop growth. The most important thing is the composition of the soil, which includes nitrogen, phosphate and potassium. Ongoing work in this area includes product selection models using machine learning techniques (random forests, tree pruning, artificial neural networks). In this paper, a recommendation model has been developed using deep learning techniques to obtain accurate information about the amount of suitable land and their added value, as well as crop estimation. It provides better accuracy than existing models. Existing data are reviewed to help farmers estimate crop yields. Take soil and climate differences into account to estimate yields. This project introduces a Python-based system that uses cunning techniques to predict profits while using limited resources. This work combines SVM machine learning algorithm with LSTM and RNN deep learning algorithms. © 2023 IEEE.

关键词： Decision trees

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Robust Image Registration Using Deep Reinforcement Learning

Robust Image Registration Using Deep Reinforcement Learning

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Murugan, R. Srivastava, Manish Rajput, Gaurav Kumar Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

The improvement of deep reinforcement learning based totally image registration strategies has made notable development in recent years for registrations of scientific photos with massive depth disparities and complex patterns. It has enabled accurate registration with fewer user-defined parameters than conventional strategies. Robust photograph registration in clinical imaging requires addressing each algorithmic necessity and complex multimodality packages. This paper reviews more than a few recent deep reinforcements gaining knowledge of based methods for achieving robust photograph registration in scientific imaging responsibilities. In particular, the paper appears on multimodal photo retrieval, sample getting to know, and segmentation. It also critiques using switch studying and area versions and discusses destiny challenges and possibilities in this subject. © 2024 IEEE.

关键词： algorithmic development opportunities requirements traditional

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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An Innovative Method to Detect Cyberbullying on Social Media 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

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Improving Bandwidth Utilization for Underwater Fiber Optic Networks Through Intelligent Optimization Techniques

Improving Bandwidth Utilization for Underwater Fiber Optic N...

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

作者： Sakhare, Nitin N. Yadav, Rakesh Kumar Rajesh, A. Hassan, Inzimam Ul Vinithra Banu, T. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Underwater Fiber Optic Networks (UFONs) are getting increasingly more important because of the emergence of the marine economic system, the enlargement of ocean exploration sports, and the need for reliable net get entry to. However, their bandwidth utilization is negative due to insufficient useful resource allocation techniques, constrained community design facts, and restricted expertise about consumer traffic styles. This paper proposes clever optimization techniques to improve the bandwidth usage of UFONs. These strategies are primarily based on gadget studying strategies along with neural networks and particle-swarm optimization. Numerous performance metrics are used to assess the proposed techniques. Simulations and experiments are performed to compare the overall performance of the proposed techniques with conventional techniques. Effects show the proposed smart optimization strategies are able to enhancing the bandwidth utilization of UFONs by means of as much as sixteen%. The proposed shrewd optimization strategies can provide significant enhancements for actual-time aid allocation, dynamic network control, and monitoring of underwater traffic styles. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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Steganalysis on Dual-Layer Security of Messages Using Steganography and Cryptography 15

Steganalysis on Dual-Layer Security of Messages Using Stegan...

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15th International Conference on information technology and Electrical engineering, ICITEE 2023

作者： Shamsi, Zeba Saha, Anish Kumar Patgiri, Ripon Singh, Khoirom Motilal Singh, Laiphrakpam Dolendro National Institute of Technology Dept. of Computer Science and Engineering Assam Silchar788010 India Indian Institute of Information Technology Dept. of Computer Science and Engineering Manipur Senapati795002 India

ISBN: (纸本)9798350304466

Recently, Bansal et al. proposed a dual-layer message security technique using steganography and cryptography. An index table maps each text data to an index point and an elliptic curve point. The text data are encrypted using elliptic curve cryptography, and the corresponding index values are embedded in a cover image using the Least Significant Bit Inversion technique. The proposed paper breaks the security of the key exchange technique in the Bansal et al. method and the secret text is extracted. © 2023 IEEE.

关键词： Steganography

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Evaluation of Distance Estimation Using Phase information of OFDM Signal 100

Evaluation of Distance Estimation Using Phase Information of...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Liu, Jingwei Kikuchi, Noriyasu Kubo, Nobuaki Tang, Suhua The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Kanagawa Institute of Technology Department of Mechanical Engineering Kanagawa Japan Tokyo University of Marine Science and Technology Information and Communication Engineering Laboratory Tokyo Japan

ISBN: (纸本)9798331517786

In urban areas, due to the obstruction/reflection of GNSS signals by roadside buildings, position accuracy is greatly degraded, or even outage occurs when no enough satellites are available. To address this problem, vehicles and roadside units have been used as positioning anchors to estimate pedestrian position. Although previous methods exploited channel state information, which is superior to received signal strength for distance estimation, they rely on signal attenuation characteristics, and the accuracy is limited by temporal resolution and multipath propagation. This work focuses on using the phase information of V2X OFDM signals to estimate distance differences more robustly. We investigated the impact of the sampling rate, a crucial parameter, and conducted simulation evaluations using 3D maps and ray-tracing. Additionally, we built a testbed and performed evaluations with real-world OFDM signal data, which demonstrates that phase information can significantly improve the accuracy of distance measurements. © 2024 IEEE.

关键词： Frequency division multiplexing

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Multi-Agent System Based on Stacking Technique for Rice Yield Prediction 3

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Seireg, Hayam R. Omar, Yasser M. Elmahalawy, Ahmed Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf Egypt Aastmt Faculty of Computing and Information Technology Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350323511

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA. © 2023 IEEE.

关键词： Multi agent systems

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Machine Learning Supported Network Attack Detection with a Novel Method Based on Random Forest-Based Feature Fusion 17

Machine Learning Supported Network Attack Detection with a N...

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17th International Conference on information Security and Cryptology, ISCTurkiye 2024

作者： Cinar, Cihan Dogru, Ibrahim Alper Atacak, Ismail Information Security Engineering Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

Nowadays, Innovative techniques, including artificial intelligence and machine learning (ML), have been employed in attack detection systems across numerous domains to create effective detection mechanisms that identify attacks more quickly and accurately. In this study, we propose a novel machine learning-based method that utilizes Random Forest (RF)-based feature fusion (RFBFF) to detect anomalies in computer networks. This method integrates two basic procedures: feature fusion and classification. In feature fusion, unlike traditional feature selection, all features are utilized in the creation of the new data set. This allows for the incorporation of the impact of each feature on the outcome. The feature fusion procedure generates a reduced dataset by combining similar features based on RF-derived gain scores, while the classification procedure uses this refined dataset to label the attack status in the network using various ML algorithms. Specifically, Decision Tree (DT), K-Nearest Neighbor (k-NN), Additive Logistic Regression (Logitboost), and Naïve Bayes (NB) have been employed as classification algorithms. The effectiveness of the proposed method in network attack detection has been validated by comparing it with ML methods based on RF-based feature selection (RFBFS). For a robust comparison and see the performance of the model in the real world, the NSL-KDD and UNSW_NB15 data sets, which contain data packet examples of normal or attack traffic that can be encountered in the internet environment, categorized with binary classification, were preferred as the benchmark data set. Experimental results demonstrate that the proposed approach excels in detecting network attacks, achieving an accuracy rate of 0.995 on the NSL-KDD dataset and 0.924 on the UNSW-NB15 dataset. Leveraging the gain scores of all features, even if they are relatively low, has been found to positively impact the model's overall performance. © 2024 IEEE.

关键词： Network intrusion

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High data-rate two-three inputs all-optical and gate based on FWM in highly nonlinear fiber

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Journal of Optical Communications 2024年第3期45卷 513-522页

作者： Sabbar, Adnan Abdulnabi, Saif Hasan Fakhruldeen, Hassan Falah Department of Electrical Engineering Faculty of Engineering University of Kufa Kufa Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-Sadiq University Al-Najaf Al-Ashraf Iraq Department of Electronics and Communications Engineering Faculty of Engineering University of Kufa Kufa Iraq Department of Electrical Engineering College of Engineering University of Baghdad Baghdad Iraq

In this paper, two-and-three channel all-optical AND logic gates based on four-wave mixing in a highly nonlinear fiber for 120 Gbps on-off keying signals were designed, simulated, and investigated. Simulation results show low bit error rate and consequently fairly high Q-factor at the receiving end in both two and three inputs AND gates, as well as the logic operation, is confirmed by the open eye diagrams with moderate power penalties for both designs. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Four wave mixing

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