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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Janmejay Pant Pushpa Pant Jaishankar Bhatt Devendra Singh Lalit Mohan Hitesh Kumar Pant Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Campus India Department of Geography M.B. Govt. P.G College Haldwani India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India GL Bajaj Institute of Technology and management Noida India Department of Management Studies Kumaun University Bhimtal India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Crop prediction has historically been influenced by farmers’ needs, but the impact of climate change on crop yields has made traditional methods unreliable. Factors such as soil quality and environmental conditions are no longer sufficient for determining which crops to grow. Manual predictions have proven ineffective, prompting the need for accurate crop prediction methods. Machine learning has emerged as a crucial tool for this purpose, considering various factors like soil composition, geographical location, and climate conditions. Selecting the appropriate crop based on these attributes is essential for accurate predictions. This paper examines multi-class crop prediction models employing machine learning techniques. Specifically, Xgboost and Random Forest classifiers were utilized in our analysis. While both models demonstrate good accuracy, The findings of this study indicate that both Random Forest and Xgboost models achieve relatively high accuracy rates in predicting crops from the dataset. Specifically, the Xgboost model outperforms the Random Forest model slightly, with an accuracy of 83.25% compared to 82.5%.

关键词： Accuracy Crops Manuals Predictive models Soil Data science Random forests

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Comparative Analysis of Optimization Algorithms for Antenna Selection in MIMO Systems 7th

Comparative Analysis of Optimization Algorithms for Antenna ...

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Sindhwani, Nidhi Anand, Rohit Nageswara Rao, G. Chauhan, Sudhir Chaudhary, Alka Gupta, Ankur Pandey, Digvijay AIIT Amity University Uttar Pradesh Noida India DSEU G.B.Pant Okhla-1 Campus Govt. of NCT of Delhi New Delhi India EEE Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh JNTUK Kakinada Mylavaram India AIAS Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India Department of Technical Education IET Lucknow Dr. A. P. J Abdul Kalam Technical University Lucknow Lucknow India

ISBN: (纸本)9789811988646

Since past few years, there has been a huge demand for the applications involving enormous data rate. Between them, Multiple Input–Multiple Output (MIMO) schemes have been proved to have exceptional benefits in connection with spectral efficiency. Implementation of MIMO device is intricate because of the usage of a greater number of antennas. To address this key issue, antenna selection is one attractive approach to mitigate this requirement. The upcoming radio networks need basic perception of the design concepts and regulation processes to easily control the various resources. The policies related to the assignment of resources are very crucial as far as radio communication networks are concerned as they are directed toward the Quality of Service (QoS) required at the client level. This paper investigates the concept of choice of antennas and related topics for MIMO wireless networks. In this regard, we have first proposed adaptive genetic algorithm (AGA). Secondly, antenna selection is implemented by using firefly algorithm with LTE scheduling, then we have explored the new optimization algorithm based on Adaptive Spectrum Matching (ASM), a Joint Optimization-based scheduling method for a successful Multiple Input–Multiple Output communication is considered, finally, their characteristics, advantages, performance metrics, limitations have been studied in detail, and research gaps have been identified. Moreover, to demonstrate the effectiveness of developed antenna selection schemes and to validate the results, the comparison of various antenna selection schemes is performed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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Implementation of an Early Warning System using Internet of Things and Rainfall Threshold 6

Implementation of an Early Warning System using Internet of ...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Chinamuttevi, Veera Sivakumar Prasad, K.D.V. Gowda, V. Dankan Begum, A. Yasmine Tanguturi, Rama Chaithanya Ahmed, Bashir Univeristy of Technology and Applied Sciences-IBRI Department of Engineering Oman Symbiosis Institute of Business Management Telangana Hyderabad India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Kamataka Bangalore India Sree Vidyanikethan Engineering College Department of Eie A.rangampet Andhra Pradesh India Pace Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Govt. Degree College Banihal Department of Physics Ramban Jammu and Kashmir Banihal India

ISBN: (纸本)9781665482714

Landslide hazard guideline serves as one among the various elements within a built-in processing and preparation investigation. The landslide hazard zonation studies are the critical task in the landslide risk assessment process. Also to minimize the death toll due to landslides, landslide early warning is one of the important considerations. The attempts on landslide hazard zonation have been carried out in India for the past few decades. However, the systematic landslide hazard zonation studies started in the early 1980's. The major challenge of efficiently communicating with the hardware (Pi processor) through internet or satellite connectivity depends on the availability of the resources. The major difference between this proposed architecture and the already available predicting systems is that, this is a low cost and user-friendly app interface which can communicate information with the users in a matter of seconds. A test was performed by making a model in the lab-scale (using sensor network) and artificially triggering soil slide by sprinkling water (artificial rain). To perform early warning to authorities, an android based mobile application has been created using the Internet of Things. The present application alerts the people while rainfall in the identified slope area exceeds the threshold limits. © 2022 IEEE.

关键词： Landslides

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Efficient Canine Vision: Accurate Dog Breed Classification with EfficientNet

Efficient Canine Vision: Accurate Dog Breed Classification w...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： T. Prabahar Godwin James Poonguzhali C Selvakumaran S G. Malathi Kaliappan S Nookala Venu Department Of Computer Science and Engineering Sri Sairam Institute of Technology Department of Electronics and Communication Engineering S.A. Engineering College Chennai Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Department Of Computer Science and Engineering Sri Sairam Institute of Technology Chennai Department of Electrical and Electronics Engineering Kumaraguru College of Technology Saravanampatti Coimbatore Centre for IoT Madhav Institute of Technology & Science (MITS) Gwalior Madhya Pradesh India A Govt. Aided UGC Autonomous Institute

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Understanding and classifying dog breeds is a fascinating yet challenging task due to the vast diversity within the canine species. In this project, we present a novel approach to dog breed classification utilizing the state-of-the-art Efficient Net architecture. Leveraging a comprehensive dataset comprising 133 distinct breeds, our method aims to accurately identify and categorize each breed with high precision. Efficient Net’s innovative scaling method allows us to strike a balance between model complexity and computational efficiency, enabling robust classification performance even with limited computational resources. Through meticulous preprocessing techniques and data augmentation strategies, we enhance the model's ability to generalize across diverse breed characteristics, minimizing the risk of overfitting. Our experimental results demonstrate the efficacy of the proposed approach, achieving impressive classification accuracy on the challenging dog breed dataset. We conduct extensive evaluations and comparisons with existing methodologies, showcasing the superiority of Efficient Net in handling complex classification tasks with large-scale image datasets. Furthermore, we provide insights into the interpretability of the model's predictions, shedding light on the learned representations and key features contributing to breed classification. This project not only advances the state-of-the-art in dog breed classification but also contributes to our understanding of deep learning techniques applied to animal recognition tasks. In sum up, our work highlights the potential of leveraging advanced neural network architectures like Efficient Net for comprehensive and accurate dog breed classification, paving the way for applications in veterinary medicine, pet care, and animal welfare.

关键词： Deep learning Adaptation models Image recognition Computational modeling Dogs computer architecture Data models

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet

Detection of Mango Leaves Disease using CSUBW based Residual...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Sunil Kumar V Jyothi A P Nagamani H S Sumanth S Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Faculty of Engineering and Technology Ramaiah University of Applied Sciences Bengaluru India Department of Computer Science Govt. Smt VHD Central Institute of Home Science (MCU) Bengaluru India Department of Computer Science and Computer Applications Government College for Women (Affiliated to Bengaluru North University) Kolar India

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy.

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Applications of Deep Learning (DL) Techniques in Detecting Breast Cancer and Malignant Cells 5

Applications of Deep Learning (DL) Techniques in Detecting B...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Chakrapani, I.S. Tyagi, Neha Tyagi, Swati Kunekar, Pankaj Padmaja, D. Lakshmi Pant, Kumud PRR&VS Govt. College Department of Zoology Vidavalur Andhra Pradesh Nellore India Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India University of Delaware NewarkDE United States Vishwakarma Institute of Technology Pune India Anurag University Department of IT Telangana India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

According to the most recent statistics, the most common type of cancer globally is the breast carcinoma and kills close to 900,000 people annually. Early and accurate diagnosis of the illness can increase the likelihood of successful treatment and lower the mortality rate. In fact, an early diagnosis can help stop it from spreading and prevent the premature victims from getting it. Researchers who study cancer have a number of difficulties when attempting to differentiate between benign and malignant tumors as well as attempting to make judgments about benign and metastatic breast carcinoma. Examine the effectiveness of automated deep learning algorithms at identifying malignant cells in women's breasts and cancer stage. This paper suggests applying deep learning algorithms to whole-slide pathology images in order to possibly increase diagnostic efficacy and accuracy. The convolutional neural networks (CNN), sparse auto encoders (SAE), and stacked sparse auto encoders are illustrations of techniques of deep learning were used in this research work. There are numerous public mammographic databases available. The methods discussed in this paper are put to the test using the mini-MIAS mammographic database. The stacked sparse auto encoder performs better, this method has to be tested in a clinical setting before being used. It has higher accuracy and precision as compared to CNN and SAE. Better diagnostic performance was achieved by several deep learning methods. Deep learning algorithms are used to better reliably identify tiny tumors while detecting breast cancer via a mammogram. © 2022 IEEE.

关键词： Diseases

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IoT-Based Diagnosis and Recommendation System for Chronic Diseases Using Patient Health Records

IoT-Based Diagnosis and Recommendation System for Chronic Di...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： S. K. UmaMaheswaran Durgaprasad Gangodkar V. Samatha I. S. Chakrapani Dharamvir Dharam Buddhi Department of Mathematics Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Applications DKW Govt. College for Women (A) Nellore AP India Dept.of Zoology PRR&VS Govt College AP Dept. of MCA The Oxford College of Engineering Bangalore Uttaranchal Institute of Technology Uttaranchal University

The proliferation of IoT applications, notably in the sphere of health care, has led to discussion of patient health records utilising data gathered from IoT-connected devices. Biological data from patients' medical records is mined for health analysis and diagnosis. Certain types of illness, known as chronic illnesses, are completely quiet yet devastating if left untreated. Recent years have seen an uptick in interest from academics in the utilization of patient health information data for the pre-emptive identification of chronic diseases. On the other side, healthcare and medical assistance have benefited enormously from the implementation of recommender systems that use machine learning techniques. Using an Internet of Things device, this research implemented a medical recommendation system to aid in the early diagnosis and management of chronic conditions. The current technique made use of the dataset of digitised patient health records that is housed in the Physio Net data repository. The current dataset contains patient health records that have been documented in accordance with the diseases that have been identified and the doctor's diagnosis. The recommended technique uses K-nearest neighbour classification to identify the kind of ailment before using collaborative filtering to select the optimal course of treatment for patients. The outcomes of using the suggested methodology show that this technique, which is based on using patient symptom similarity, produces better results than previous methods and has a high precision in diagnosing and forecasting chronic illnesses. After determining the kind of disease using the closest neighbour classification method, the collaborative filtering strategy is utilised to select the optimal course of therapy for patients. The outcomes of using the suggested methodology show that this technique, which is based on using patient symptom similarity, produces better results than previous methods and achieves better accuracy

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Phase transition and birefringence studies on some ferroelectric liquid crystals

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Materials Today: Proceedings 2023年 89卷 126-129页

作者： Syed Habeebulla Hassain C.M. Subhan K. Nagi Reddy S. Manu C.V. Yalamaaggad K. Fakruddin Department of Physics MES College of Arts Commerce and Science Bengaluru Karnataka India Govt. Polytechnic Madakasira India Department of Physics K.L.E.Society’s S Nijalingappa college Bengaluru India Centre for Soft Condensed Matter Sciences Bengaluru India Department of Physics Ghousia College of Engineering Ramanagaram Karnataka India

In the present studies the homologous series of hydroxy phenylamino methyl phenoxy benzoate liquid crystals (n = 8,9 and m = 6) are used. The temperature variation of density is measured. Using density data the phase variants in the compounds are identified. All the compounds exhibit first-order phase transitions. By Newton rings method the birefringence is measured directly in smectic and nematic phases. The advantages of the newton ring technique are presented over the conventional methods for the determination of orientational order parameter. The orientational order parameter attained by the newton rings scheme is equated through the literature data, the results are discussed.

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Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks

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Discover Applied Sciences 2024年第1期6卷 1-13页

作者： Kishore, K. Krishna Rajasekaran, Arun Sekar Keshta, Ismail Byeon, Haewon Bhatt, Mohammed Wasim Irshad, Azeem Ofori, Isaac Department of Electronics and Communication Vasavi College of Engineering Hyderabad India Department of Electronics and Communication Engineering SR University Warangal India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea Model Institute of Engineering and Technology Jammu India Asghar Mall College Rawalpindi Higher Education Department (HED) Govt. of the Punjab Rawalpindi Pakistan University of Mines and Technology (UMaT) Tarkwa Ghana

Cognitive radio networks have emerged as a promising solution to the growing problem of spectrum scarcity and inefficient spectrum utilization. By enabling secondary users to opportunistically access underutilized frequency bands while avoiding harmful interference to primary users, cognitive radio networks offer the potential for more efficient and dynamic spectrum allocation. In this paper, we propose an intelligent approach to dynamic spectrum access using fuzzy logic in cognitive radio networks. The key challenge in cognitive radio networks lies in spectrum sensing, decision-making, and channel selection, where uncertainties and variations in the radio frequency spectrum are prevalent. To address these challenges, we employ fuzzy logic as a powerful tool to handle the inherent imprecision and ambiguity in the decision-making process. Fuzzy-based spectrum sensing algorithms allow for robust detection of spectrum opportunities by considering the “fuzziness” in the received signal strength and noise conditions.

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Decay properties of undetected superheavy nuclei with Z>110

arXiv

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arXiv 2023年

作者： Jain, A. Sharma, P.K. Jain, S.K. Akrawy, Dashty T. Saxena, G. Department of Physics Manipal University Jaipur Jaipur303007 India Govt. Women Engineering College Ajmer305002 India College Jaipur302004 India Govt. Polytechnic College Rajsamand313324 India Physics Department College of Science Salahaddin University Kurdistan Erbil44001 Iraq Becquerel Institute for Radiation Research and Measurements Kurdistan Erbil Iraq Department of Physics Faculty of Science University of Zagreb Bijenička c. 32 Zagreb10000 Croatia

A comprehensive study of favoured and unfavoured α-decay, cluster decay, weak-decay along with spontaneous fission in undetected superheavy nuclei within the range for proton number 111≤Z≤118 and neutron number 161≤N≤192 is performed. Half-lives for various mentioned decays are estimated with good accuracy on the basis of NUBASE2020 and are found in excellent match with the known half-lives. α-decay mode is found most probable in this wide range and correspondingly potential α-decay chains are reckoned. Peculiarly, the chances of cluster emission, as well as weak-decay, are also anticipated in this region of the periodic chart which open new pathways of detection of superheavy nuclei. Copyright © 2023, The Authors. All rights reserved.

关键词： Nuclear reactions

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