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3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model

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SN computer science 2025年第3期6卷 1-17页

作者： Anoop, G.L. Nandini, C. Naresh, E. Department of Computer Science and Engineering Christ University Karnataka Bengaluru India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The advancement of crop yield prediction through artificial intelligence (AI) has gained significant attention. However, the existing AI-based approaches for maximizing agricultural productivity, specifically in crop yield prediction, have not consistently delivered satisfactory results. In response to this challenge, we propose a novel framework named as Three Tier Feature Learning with XLnet based Crop Prediction (3TFL-XLnet-CP) that enhances agricultural productivity by accurately predicting crop yield. The 3TFL-XLnet-CP framework employs a three-tier feature learning approach in combination with the powerful XLnet transformer-based crop prediction model. The three-tier feature learning involves the integration of Spiking Neural Network (SNN), Graphical Neural Network (GNN), and Convolutional Neural Network (CNN) to extract distinct feature vectors from the preprocessed data. These feature vectors are then concatenated using Jaccard Similarity to measure their similarity score. Additionally, a weighted Loss function is introduced to optimize feature learning, further enhanced by a novel self-adaptive Spider Monkey Optimization algorithm (SASMO). The concatenated features are subsequently fed into the classification layer for making precise crop yield predictions. The proposed model is implemented using the Python platform and evaluated against existing models such as ANN, RNN, DNN, and BiLSTM. The comparison demonstrates the superiority of our proposed 3TFL-XLnet-CP framework in accurately predicting crop yield. © The Author(s) 2025.

关键词： 3TFL-XLnet-CP Convolutional neural network Crop yield prediction Graphical neural network Spiking neural network

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Prior detection of Melanoma skin cancer using Deep Learning 4

Prior detection of Melanoma skin cancer using Deep Learning

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kathuria, Kajol Sahoo, Anita Jain, Chakresh Kumar Jaypee Institute of Information Technology Computer Science & Engineering and Information Technology Noida India Biotechnology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798350364729

Anomalous growth of skin cells caused due to harmful Sun's rays is predominant form of cancer. According to WHO skin cancer is the fifth most common cancer across the world. In 2020, 324635 new cases were accounted of both sexes world wide & 57043 deaths have been reported by Globocan 2020. Skin cancer is categorized in to three: Basal Cell carcinoma, Melanoma & Squamous Cell Carcinoma. Furthermore Melanoma is the most lethal malignancies in the world with very less chances of Survival. Early detection of skin cancer is crucial as it provides treatment options & better survival rates, conversely it may spread to other organs & can access lymphatic vessels. Assuming the danger of skin cancer, researchers have developed many early detection methods. Colour, symmetry, form, size, & other lesion criteria are utilised to detect & distinguish between benign & malignant skin cancers. Convolutional neural networks (CNN) are one of the most effective approaches for diagnosing melanoma today. This paper provides an overview of deep learning algorithms for the early diagnosis of skin cancer. The topic of skin cancer diagnosis-related research articles from credible publishers was investigated. The table shows a comparative examination of skin cancer detection using deep learning algorithms. © 2024 IEEE.

关键词： Lung cancer

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Software defined network (SDN) based data server computing system

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International Journal of Information technology (Singapore) 2025年第1期17卷 607-613页

作者： Madhura, K. Sekhar, Gillala Chandra Sahu, Amaresh Karthikeyan, M.P. Khurana, Saniya Shukla, Meenu Vashisht, Nitish Information and Communication Technology Manipal Institute of Technology Mahe Bengaluru India Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Ajay Binay Institute of Technology Odisha India Department of Computer Science and Information Technology Jain (Deemed to be University) Karnataka Jain Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Rajpura India

The growing clientele and broadband demand lead to several technical restrictions in ensuring stable connections between data centers. The data on existing network devices such as switches, routers, and gateways must be routed to some particular point commonly known as software-defined network (SDN). SDN is defined as a networking approach that makes use of software-based controllers or application programming interfaces (API’s) to communicate with existing hardware and provide paths to traffic in the network. In order to overcome technical restrictions in the network, the given paper introduces an application-based enabled open flow information center network layout to accomplish transport load allocation and higher efficiency. In order to mitigate noise in the network, the proposed approach employs a data centre network (DCN) and implements it using SDN controller such as OpenDaylight (ODL). ODL is an open-source platform for automating networks of any size and scale. It allocates traffic requests among the linked servers, thereby ensuing higher throughput, optimization of load balancing, and versatility in SDN’s. Lastly, the performance of the proposed approach is validated by assessing the throughput rate (Mbps) and average delay (ms) prior to load balancing (BLD) and after load balancing (ALD). The results show that the proposed approach leads to higher throughput after load balancing optimization using ODL. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： DCN Load balancing ODL SDN Throughput

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Existing Techniques For Fundamental, Technical And Sentimental Analysis For Stock Investement

Existing Techniques For Fundamental, Technical And Sentiment...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Muzaffar, Mohammad Shaazan Niranjan, Shivam Joel, Kadugula Kumar, Anuj Baniya, Pashupati Agrawal, Atul Sharda University Department of Computer Science and Engineering Greater Noida India Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

In today's complex financial world, where things like global economics, company strategies, and how people feel about things can all affect the stock market, buying and selling stocks has become really popular. People are attracted to it because they think they can make a lot of money quickly. But there's a big problem: while making money is exciting, there's also a big chance of losing money because the stock market can be very unpredictable. Trying to figure out what the stock market will do next is super important, but also really hard. There are lots of different things that can affect it, like what's happening in the world, how well companies are doing, and even how people are feeling about investing. So, our paper is all about using existing Techniques (like AI and machine learning) to try to predict what the stock market might do. This paper explores three main ways of doing this.i.e, Fundamental, Technical and Sentimental Analysis: looking at basic stuff about companies, studying the patterns of stock prices, and analyzing how people feel about investing. This paper will take in account what other researchers have already found out about these things, our main goal is to help other researchers to understand more about predicting the stock market. The aim of this paper is to initiate a comprehensive discourse on this subject, inviting widespread participation to equip individuals with the knowledge and confidence needed to make informed investment decisions, even among the unpredictable fluctuations of the stock market. © 2024 IEEE.

关键词： Marketplaces

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Detecting Deepfakes and Artificial Intelligence-Generated Art 2

Detecting Deepfakes and Artificial Intelligence-Generated Ar...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Rajeev, Faaizah Panapparambil Shetty, Sujala Deepak Computer Science and Engineering Birla Institute of Technology and Science Pilani – Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798331504106

Recent years have seen innovative advancements in Artificial Intelligence (AI) in the realms of image and video processing, speech and audio recognition, and pattern recognition, leading to the boom in AI art and deepfakes. The evolution of ChatGPT, Dalle-2, and Midjourney has sparked debate about whether AI art qualifies as true art and whether it will eventually displace human artists and creators. AI art generation models are trained on large datasets, often consisting of copyrighted art. Due to this, the resulting picture produced is frequently a manipulated version of the art piece, namely through various techniques - CutMix, Adversarial Data Poisoning, Inpainting, and Style Transfer. This manipulation constitutes a violation of the rights held by the artists. On a similar note, deep fakes pose threats to privacy and security. Society is entering a new age of misinformation and confusion, where they are victims of deepfake phishing scams, identity theft, and malicious political interference. These issues necessitate the development of state-of-the-art algorithms to detect real-life photos and videos, as well as painting and art manipulation. This paper dives deep into the topic of AI art and Deepfake detection, highlighting the most efficient machine learning classification techniques on the DeepfakeArt Challenge and Deepfake Detection Challenge dataset. It aims to highlight the best algorithm that provides reliable results and assists future research in this newly emerging area. ©2024 IEEE.

关键词： Phishing

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Sea clutter suppression via cuttable encoder-decoderaugmentation network

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Journal of Systems engineering and Electronics 2024年第6期35卷 1428-1440页

作者： ZANG Chuanfei WANG Yumiao WANG Xiang XU Congan CUI Guolong School of Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Advanced Technology Research Institute Beijing Institute of TechnologyJinan 250300China Naval Aviation University Yantai 264000China

This paper considers the problem of sea clutter *** propose the cuttable encoder-decoder-augmenta-tion network(CEDAN)to improve clutter suppression perfor-mance by enriching the contrast information between the target and ***,the plug-and-play residual U-block(ResUblock)is proposed to augment the feature representation ability of the clutter suppression *** CEDAN first extracts and fuses the multi-scale features using the encoder and the decoder composed of the ***,the fused features are processed by the contrast information augmenta-tion module(CIAM)to enhance the diversity of target and clutter,resulting in encouraging sea clutter suppression *** addi-tion,we propose the result-consistency loss to further improve the suppression *** result-consistency loss enables CEDAN to cut some blocks of decoder and CIAM to reduce the inference time without significantly degrading the suppression *** results on measured and simulated data show that the CEDAN outperforms state-of-the-art sea clutter suppression methods in sea clutter suppres-sion performance and computation efficiency.

关键词： radar sea clutter suppression deep learning

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Sulfhydryl-functionalized COF-based electrolyte strengthens chemical affinity toward polysulfides in quasi-solid-state Li-S batteries

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Carbon Energy 2024年第9期6卷 221-234页

作者： Linnan Bi Jie Xiao Yaochen Song Tianrui Sun Mingkai Luo Yi Wang Peng Dong Yingjie Zhang Yao Yao Jiaxuan Liao Sizhe Wang Shulei Chou Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhouChina National and Local Joint Engineering Research Center of Lithium-ion Batteries and Materials Preparation Technology Key Laboratory of Advanced Battery Materials of Yunnan ProvinceFaculty of Metallurgical and Energy EngineeringKunming University of Science and TechnologyKunmingChina Institute for Carbon Neutralization College of Chemistry and Materials EngineeringWenzhou UniversityWenzhouChina School of Materials and Energy University of Electronic Science and Technology of ChinaChengduChina School of Materials Science and Engineering Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic MaterialsShaanxi University of Science&TechnologyXi'anChina

For lithium-sulfur batteries(Li-S batteries),a high-content electrolyte typically can exacerbate the shuttle effect,while a lean electrolyte may lead to decreased Li-ion conductivity and reduced catalytic conversion efficiency,so achieving an appropriate electrolyte-to-sulfur ratio(E/S ratio)is essential for improving the battery cycling efficiency.A quasi-solid electrolyte(COF-SH@PVDF-HFP)with strong adsorption and high catalytic conversion was constructed for in situ covalent organic framework(COF)growth on highly polarized polyvinylidene fluoride-hexafluoropropylene(PVDF-HFP)***-SH@PVDF-HFP enables efficient Li-ion conductivity with low-content liquid electrolyte and effectively suppresses the shuttle *** results based on in situ Fourier-transform infrared,in situ Raman,UV–Vis,X-ray photoelectron,and density functional theory calculations confirmed the high catalytic conversion of COF-SH layer containing sulfhydryl and imine groups for the lithium *** plating/stripping tests based on Li/COF-SH@PVDF-HFP/Li show excellent lithium compatibility(5 mAh cm^(-2) for 1400 h).The assembled Li-S battery exhibits excellent rate(2 C 688.7 mAh g^(-1))and cycle performance(at 2 C of 568.8 mAh g^(-1) with a capacity retention of 77.3%after 800 cycles).This is the first report to improve the cycling stability of quasi-solid-state Li-S batteries by reducing both the E/S ratio and the designing strategy of sulfhydryl-functionalized COF for quasi-solid *** process opens up the possibility of the high performance of solid-state Li-S batteries.

关键词： lithium-sulfur batteries low electrolyte-to-sulfur ratio polysulfide shuttle PVDF-HFP/COF

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Enhancing the Capability of Single Error Correction and Double Error Detection Method 2

Enhancing the Capability of Single Error Correction and Doub...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Sadi, Muhammad Sheikh Molla, M.D. Peyal Asraf, Amanullah Ali, Md. Liakot Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798331508227

This paper proposes a new approach to tolerate double-bit soft errors. Soft error is becoming a matter of concern for computer systems. Critical systems and systems operated in electronic hostile environments are at high risk due to the sudden occurrence of soft errors. This paper proposes a new method for correcting double-bit soft errors in an n-bit dataword where n can be any integer number. The most efficient part of this paper is the temperate use of redundant bits. In this paper, the XOR method is used along with the SECDED (Single Error Correction - Double Error Detection) code to enhance the capability of SECDED toward double-bit error correction. This paper corrects all two-bit errors and can also correct almost every type of triple-bit error. ©2024 IEEE.

关键词： Errors

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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Fortifying Cloud Data Security with the Maritime Search and Rescue Algorithm 2

Fortifying Cloud Data Security with the Maritime Search and ...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Parameswaran, T. Swapna, J. Prabu, S. Kaliappan, S. Vit University School Of Computer Science And Engineering Chennai India Cmr University Lakeside Campus School Of Engineering And Technology Department Of Computer Science And Engineering Bangalore India Veltech Rangarajan Dr.Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamilnadu Chennai India Mahendra Institute Of Technology Department Of Ece Namakkal India Lovely Professional University Division Of Research And Development Punjab Phagwara India

ISBN: (纸本)9798350381665

Cloud computing has fundamentally transformed the accessibility and payment models of services within distributed system environments. In response to heightened security concerns, cloud service providers offer diverse resources spanning Infrastructure, Software, Platform, and Database services, supported by security measures such as Service Level Agreements (SLAs), access control rules, and cryptographic methods. This paper introduces a novel data encryption technique, termed Data Security in Cloud Computing using Maritime Search and Rescue Algorithm (DSCC-MSRA). By integrating an enhanced cypher block chaining algorithm with logical substitution and the Maritime Search and Rescue Algorithm (MSRA) for key generation, this method provides a unique approach to encryption. Through rigorous optimization and analysis, it has undergone significant refinement, with comparisons against industry-standard algorithms showcasing its efficacy. The primary objective is to elevate encryption procedures and introduce complexity to cryptanalysis, ultimately enhancing data security in cloud computing. © 2024 IEEE.

关键词： Cryptography

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