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Review on Crop Prediction Using Deep Learning Techniques

Journal of Physics: Conference Series 2021年第1期1767卷

作者： M K Dharani R Thamilselvan P Natesan PCD Kalaivaani S Santhoshkumar Assistant Professor Department of Computer science and Engineering Kongu Engineering College Erode Professor and Head Master of Computer Applications Kongu Engineering college Erode Professor and Head Computer Technology (UG) Kongu Engineering college Erode Assistant Professor (SLG) Department of Computer science and Engineering Kongu Engineering College Erode Assistant Professor (SRG) Department of Computer science and Engineering Kongu Engineering College Erode

Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.

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Detection of crop disease using optimization model in biodiverse environment

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AIP Conference Proceedings 2023年第1期2855卷

作者： Dixit Baravaliya Vipul Vekariya Ishan Rajani Research Scholar Parul Institute Of Engineering And Technology Vadodara 391760 India Dean & Principal - FET Parul University Vadodara 391760 India Department of computer science and engineering Assistant Professor at Parul University Vadodara 391760 India

Diseases of the crop and insect pests are among the main causes of crop loss which is dangerous for agricultural production. Disease recognition is more difficult on the field because it has a complex background and different light intensity. Initial recognition and pests identification can significantly reduce the financial losses caused by pests. Using convolution neural networks, crop diseases can be automatically identified. The identification of a disease is often based on signs such as lesions or spots found in various slices of a plant. The size, color and the amount of these spots can define in great detail the disease that has killed the crop. Public data set is used as a data set. Experimental environment Model works on is a biodiverse environment.

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Empowering leadership Qualities of engineering Students through Initiatives

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Procedia computer science 2020年 172卷 393-396页

作者： Hema Mahajan Rahil Hussain Santosh Madeva Naik Kannaiah 2nd Year ECE Student Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India

engineering Education is not delimited between its curriculums. However, it also needs several alternative skills to grab opportunities and help students to face any situation in the world. Our Institute has always encouraged having smart projects on the campus for bringing transformation in engineering education. It is collaborated with the IUCEE (Indo Universal collaboration for engineering Education) and began several initiatives that prove students ability, enhancing students skills for their carrier. Leadership is one of the skills that are very much important for students to achieve their goals in life. This paper can make a case study for many initiatives, its impact, and emerging skills of the students to make them better in the future. This study emphasized more on the leadership skills and its importance that each student ought to have. Institute has Speed, Scale, EPICS, SSG, and toastmaster programs on the campus where various students from different disciplines have joined and enhanced their leadership skills.

关键词： Leadership Scale Speed EPICS Toast Master

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Investigation of Strength and Security of Pseudo Random Number Generators

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： K Sathya J Premalatha Vani Rajasekar Assistant Professor Department of Computer Technology- UG Professor Department of Information Technology Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Erode India-638060

Security is a key factor in today's fast communicating world. Many cryptographic algorithms are tested and put into use efficiently. Random numbers are used in diverse forms like nonces, secret key, initialization vector, etc. They find place in encryption, digital signature, hashing algorithms. A deterministic algorithms takes an intial seed value as input and produces pseudo random numbers with falsely induced randomness. This research work extensively surveys large set of state-of-the-art PRNGs and categorizes them based on methodology used to produce them. We compared their statistical results obtained from various statistical test tools like NIST SP 800-22, TestU01. Finally, security analyses of various PRNGs were done quantitatively for their key space, key sensitivity, entropy, speed of bit generation, linear complexity. At last, we concluded the results with some future directions for researchers to carry out their research in improving the PRNGs.

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Secure K-NN Query on Encrypted Cloud Data with Multiple Keys

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： R. Vignesh D. Deepa Suja Cherukullath Mana B. Keerthi Samhitha Assistant Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology

Cloud computing has become an undeniably famous assistance for its adaptability and versatility, which rouses numerous associations, foundations and organizations to want to re-appropriate data administrations to cloud stage. Simultaneously, much consideration has been paid to adapt to the extraordinary security and protection issues in recloud cloud. The general methodology is to scramble data by the data owner (DO) before re-appropriating; the approved query user play out an unpredictable arrangement of encryption and decoding tasks during question execution. In any case, the above plans have accepted that the question clients are completely trusted and have the entrance to the key for scrambling and unscrambling recloud data. It will achieve a few issues in reality. So we propose a novel plan for secure KNN inquiry on encoded cloud data with various keys, in which the data owner and each question client all hold their own various keys, and don't impart them to one another; in the interim, the data owner scrambles and decodes re-appropriated data utilizing the key of his own. Our plan is built by set of conventions to jelly the data classification and question over scrambled cloud data without key-sharing.

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Distributed Ensemble based Deep Learning architecture for Intrusion Detection against Cyber attacks

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： M Kavitha K Elamukhil R Ajeeth R Ashwin V Balasubramaniam Assistant Professor Department of Computer Science and Engineering Final Year Computer Science and Engineering Sri Krishna College of Engineering and Technology

The increasing scale and importance of web contact around the Internet has increased the need for improved cyber security defence against cyber attacks. On modelling the machine learning based intrusion detection system, features of the attacks helps to discover, determine and identify unauthorized behaviour. Behaviour modelling has been used as training model to detect the evolving attacks to the servers or learning model has been constructed to build a training model to identify the intrusion in the network on basis of signature of the attacks. However machine learning model fails in handling attacks propagating on the applications with large scale or large dimensional data which further leads to high false alarm rate. In order to tackle those issues, a new distributed ensemble based deep learning architecture has been employed using Convolution Neural Network, Recurrent Neural Network and Multilayer Perceptron towards intrusion detection on cyber attacks on the web servers. Convolution Neural Network, Recurrent Neural Network and Multilayer Perceptron has been modelled as training model to detect the intrusion, trained model will generate best model which acts as classifier or prediction model for cyber attacks. The particular model will be employed further to detect the intrusion propagating in the web server. The proposed ensemble based deep learning architecture is compared to state-of-the-art approaches on performance measures such as precision, recall, and f measure on true positive, false positive, true negative, and false negative computations using the KDD CUP 99 dataset.

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Implementation of station vigilance using RF for railway passengers

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AIP Conference Proceedings 2023年第1期2523卷

作者： S. Mahaboob Basha A. Arun T. D. Subha V. Bhuvaneswari D. KalaiSelvi J. Navin Sankar 1Assistant Professor Department of Electronics and Communication Engineering R.M.K. Engineering College R.S.M Nagar Kavaraipettai Tamil Nadu India 2Assistant Professor Department of Computer Science and Engineering SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu India 3Assistant Professor Department of Electronics and Communication Engineering R.M.K. Engineering College R.S.M Nagar Kavaraipettai Tamil Nadu India 4Assistant Professor Department of Electronics and Communication Engineering SRM Institute of Science and Technology City Campus Vadapalani Chennai Tamil Nadu India 5AP Department of Electronics and Instrumentation Engineering R.M.D. Engineering College R.S.M Nagar Kavaraipettai Tamil Nadu India 6Application Engineer Entuple Technologies Pvt Ltd Bangalore Karnataka India

The main aim of this work is to make the passengers to reach the station in the particular time. The microcontroller is used for prediction alert purpose. Nowadays frequently, during night time people are sleeping in trains and miss their destination station are occurred. Hence, the valuable time is absent. Therefore, using AT89C52 microcontroller, RF controller and RF receiver module the project was designed to avoid such mistakes to alert the passengers and to wake up at the correct station by getting vibration automatically. The objective of this project is to use 89C52 microcontroller to alert the passengers about the station which he/she want to reach mechanically, using RF modules. In front of, 5Km ahead of the station may have transmitter tag to transmit the zone computing by RF signals. A receiver module placed in the seat to get the zone information, by fixing a watch which is to be used to alert the passenger with small vibration to awake.

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Machine learning based calculation of refractive index of polyethylene glycol polymer

Results in Chemistry

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Results in Chemistry 2025年 16卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Hardik Doshi H.S. Shreenidhi Manoranjan Parhi Devendra Singh Prabhjot Singh Bilakshan Purohit Kamal Kant Joshi Ahmad Abumalek Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi Universitly Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science and Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India Department of Computer science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 Uttarakhand India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Allied Science Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun Uttarakhand India Faculty of Engineering Balkh University Balkh Afghanistan

This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. A databank comprising 99 experimental measurements were utilized to train and assess multiple algorithms, including Extra Trees, Random Forests, Decision Trees, Ensemble Learning, AdaBoost, K-Nearest Neighbors, Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Linear Regression, and Elastic Net. The dataset demonstrated a strong correlation between molecular weight and refractive index ( r = 0.64), and an inverse correlation with temperature ( r = −0.72). Model performance was assessed using R 2 , MSE, and AARE%, revealing that MLP-ANN, Linear Regression, and Elastic Net models yielded the highest accuracy on the testing dataset. Furthermore, SHAP analysis identified molecular weight as the dominant factor influencing refractive index predictions. The proposed approach significantly reduces the need for experimental measurements and offers a reliable, time-efficient solution for estimating the optical behavior of PEG polymers under varying conditions.

关键词： Machine learning Artificial intelligence Polyethylene glycol Refractive index Outlier detection SHAP analysis

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Sensor integrated modules – Social distance & hygiene solutions for COVID-19 pandemic

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AIP Conference Proceedings 2024年第1期2512卷

作者： Kalyan Dusarlapudi Narasimha Raju Kuthuri Kattupalli Sudhakar Prathyusha Chennupati Shiny Syam Mallolu Sai Harshitha Padala Venkata Siva Kumari Narayanam Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Senior Assistant Professor Department of AI&DS Lakireddy Balireddy College of Engineering Mylavaram India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Communication and soft skills department Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India

The Covid-19 pandemic has opened doors to the academic community to explore more possibilities for innovations. The present paper discusses three prototypes developed for the user’s safety during the pandemic lifestyle. The modules offer alert and hygiene practices to helps users not to get affected by the Covid-19 Virus. The first module intended for user alerts to maintain social distance concerning nearest humans. The module prompts the buzzer if a person comes near within one-meter vicinity. The Second module is an automatic hand sanitiser dispenser for self-clean hands with disinfectant liquid. It uses IR sensors for hand proximity detection to trigger a pump to deliver sanitiser to the hands. The last module dispenses water into a glass. The sensors incorporated are ultrasound and IR sensors for glass detection, automatic water level and user water level control. These modules components, methodology and operation, are discussed. The cost-effective modules offer a long-standing performance with an Embedded board.

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Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming

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AIP Conference Proceedings 2024年第1期3161卷

作者： R. Rajesh Kanna T. Mohana Priya V. Sivakumar Chandrasekharan Nataraj Jishamol Thomas Assistant Professor Department of Computer Science CHRIST (Deemed to be University) Bangalore India Associate Professor School of Computing Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Senior Lecturer School of Engineering Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Research Scholar Department of Sociology and Social Work CHRIST (Deemed to be University) Bangalore India

This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem.

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