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Potato Leaf Disease Detection Method is Based on the Support Vector Machines

Potato Leaf Disease Detection Method is Based on the Support...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Animesh Srivastava Bhupender Singh Rawat Vikash Sawan Vishal Shiv Ashish Dhondiyal Department of Computer Science & Engineering Graphic Era Hill University Dehradun College of Smart Computing COER University Roorkee Department of Computer Engineering & Applications GLA University Mathura Uttar Pradesh School of Computer Application Lovely Professional University Jalandhar Punjab Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Potato production is challenged by plant diseases whose adverse effects are a reduction in the yield of the crops, and which have undesirable economic implications. Recognizing these diseases is of utmost importance since control should be applied. This paper describes a method for diagnosing diseases of the potato using Support Vector Machine (SVM) classification. A SVM is an ML algorithm that is effective in classification tasks such as classifying potato leaves in terms of their diseases. The results show that the proposed method is useful and works rather well for detecting potato plant leaf diseases. In this paper, the SVM method emphasizes detecting various potato plant leaf diseases for efficient intervention before the late stage of visual assessment for microbial-introduced disease. The main aim of this research is to assist in overcoming the limitations of existing agricultural practices in speeding up the diagnosis and control of diseases affecting potato plants. So the SVM-based methods can help farmers in making timely decisions, reducing economic losses, and increasing agricultural productivity. With linear SVM, the accuracy stands at 93.1%. Nevertheless, when non-linear SVM is employed, the accuracy rises to 95.6%.

关键词： Support vector machines Productivity Economics Visualization Accuracy Crops Real-time systems Usability Diseases Resilience

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Review on Enhancing Predictive Accuracy in Alzheimer Detection: A Hybrid Approach

Review on Enhancing Predictive Accuracy in Alzheimer Detecti...

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Delhi Section Conference (DELCON), IEEE

作者： Ambooj Ihtiram Raza Khan Parul Agarwal Harleen Kaur School of Engineering Sciences & Technology Jamia Hamdard New Delhi India Department of Computer Science and Application BVICAM New Delhi India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Effective early detection techniques are essential because to the tripled global prevalence of dementia, which is mostly caused by Alzheimer's disease (AD) and is predicted to reach 152 million by 2050. AD pathology, which has a preclinical phase lasting 10–20 years before severe cognitive impairment, is associated to about 70% of dementia cases. Early detection of AD is essential since medication trial success rates may be increased and risk factor management may avoid 40% of cases. Currently, lengthy cognitive tests that are insensitive to the pre-clinical stage are used to detect dementia. Initiatives to make open data available have gathered so much data on AD that it is now too much for humans to analyze. In order to diagnose AD accurately, machine learning (ML) has become essential, especially when it comes to early-stage AD prediction. AI and ML-driven healthcare research places a strong emphasis on automated disease prediction systems, including dementia detection. Finding those who is susceptible of ‘Alzheimer's disease’ (AD) progression is still difficult, especially in light of the disease's severity and fast advancement. Preventing the progression of the disease and planning the course of treatment depend on early detection. Reviewing AD detection research conducted since 2016 has highlighted the contribution of machine learning (ML) to improving prediction accuracy. The reviews provide information on participant demographics, data modalities, feature extraction strategies, and ML models used. By combining earlier studies, promoting critical analysis, and directing future investigations for early AD diagnosis, this thorough review helps researchers.

关键词： Pathology Accuracy Reviews Predictive models Prediction algorithms Planning Alzheimer's disease Tuning Random forests Optimization

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Juan Li Rui Li Youshui Lu Bowen Cai Yulin Cao Hang Lin Xingxing Li Chan Li School of Computer Science Qinghai Normal University Xining China School of Computer Science and Technology Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University Xian China School of the Instrument Science and Technology and School of Electrical Engineering Xi’an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis USA

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model’s feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness.

关键词： Economics Automation Accuracy Limiting Smart contracts Buildings Focusing Feature extraction Robustness Data mining

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring

DL-Based Somnolence Detection for Improved Driver Safety and...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Peddinti. Neeraja R G Kumar M. Sunil Kumar Kazi Kutubuddin Sayyad Liyakat M. Sowmya Vani Department of computer Application School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur AP Department of computer science and engineering School of Computing Mohan Babu University (erstwhile SreeVidyanikethan Engineering College) Tirupathi AP India Department of computer Science and Engineering Brahmdevdada Mane Institute of Technology Solapur Maharashtra India

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads.

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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

作者： Katariya, Jitendra Kumar Yadav, Dhananjay Gayathri, C. Kolekar, Vikas K. Daivagna, Umesh M. Pushparajesh, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research. © 2023 IEEE.

关键词： Network architecture

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

A Machine Learning Approach to Compare Causal Inference Mode...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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Study of Natural Language Processing for Sentiment Analysis 3

Study of Natural Language Processing for Sentiment Analysis

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Kavitha, C.R. Rajarajan, S.J. Jothilakshmi, R. Alaskar, Kamal Ishrat, Mohammad Devi, V. Chithra Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Bengaluru560035 India Department of Mechanical Engineering Tamil Nadu Thiruverkadu600077 India R.M.D. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai601206 India Institute of Management Department of Computer Application Maharashtra Kolhapur416003 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Department of English Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350322842

The study of opinions, sentiments, assessments, perspectives, and feelings as they are expressed in written language is the focus of sentiment analysis and evaluation mining. It is commonly the subject of text mining, information mining, and web mining because it is one of the most active study fields in natural language processing. Sentiment analysis has been applied in many different contexts, including the study of how events affect unofficial groups and the exploration of presumptions about goods and services. The importance of sentiment analysis is increasing at the same time as virtual entertainment such as surveys, group chats, web blogs, micro sites, Twitter, and interpersonal organizations. In this work, we provide the results of our preliminary studies on twitter sentiment analysis. This study seeks to distinguish subjects from sentiment in tweets. It recognizes the sentiment that refers to the particular problem using Natural Language Processing techniques. Our analysis consists of three main processes, subjective grouping, semantic affiliation, and extreme order, to characterize sentiment. By describing the syntactic relationship between sentiment dictionaries and subject, the study makes use of sentiment vocabularies. Exploratory results demonstrate that the proposed framework performs better than existing message sentiment analysis tools because tweets don't have the same architecture as traditional messages. © 2023 IEEE.

关键词： Sentiment analysis

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Review of Smart Agricultural Technologies for Precision Pest Control and Sustainable Water in Cotton Farming

Review of Smart Agricultural Technologies for Precision Pest...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Prince Charles M Pandiselvam Pandiyarajan Department of Computer Application Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Cotton production is extremely susceptible to pests, disease, and irrigation mismanagement, resulting in considerable yield loss and environmental degradation. Conventional practices like manual monitoring of pests and blanket spraying of pesticides are time-consuming, ecologically detrimental, and frequently inadequate in averting timely infestations. The intersection of Artificial Intelligence (AI), the Internet of Things (IoT), and smart water management offers a revolutionary chance towards sustainable agriculture. This paper discusses the cooperative function of IoT and AI in cotton cultivation, particularly real-time pest identification, disease classification, and dynamic irrigation management. Deep learning techniques precisely detect pests at all stages of development, whereas IoT sensors track environmental data like soil moisture, temperature, and humidity. The sensor data are used as input to machine learning algorithms that adjust irrigation schedules and predict outbreaks of disease. Furthermore, adaptive learning methods such as federated and incremental learning optimize system performance through time, allowing for ongoing improvement without violating data privacy. Major findings indicate that the integration of these technologies decreases pesticide use, saves water, and enhances crop resistance. Yet, the absence of complete integrated systems prevents the full potential from being realized. This research highlights the necessity for integrated frameworks that address pest management and water management simultaneously in real time. Closing these gaps will open the door to innovations that increase yield, enhance sustainable agriculture practices, and make cotton farming sustainable in the long term.

关键词： Temperature sensors Irrigation Production Pesticides Real-time systems Cotton Internet of Things Artificial intelligence Intelligent sensors Diseases

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Next Generation HR: Transforming Talent Management with AI and ML Innovations

Next Generation HR: Transforming Talent Management with AI a...

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Disruptive Technologies (ICDT), International Conference on

作者： Niharika Singh Pooja Singh Sudeshna Chakraborty Dept. of Master of Computer Application GL BAJAJ Institute of Technology & Management Greater Noida U.P. Department of CSE School of Computing Science & Engineering Galgotias University Greater Noida (U.P.) Department of Computer Application School of Computing Science and Engineering Galgotias University Greater Noida

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-depth analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success.

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch

Stage Wise Classification and prediction of Alzheimer diseas...

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Intelligent Technologies (CONIT), International Conference on

作者： A Prabagaran Vinay Dwivedi Neelam Jyoti Rani J Angelin Blessy P Padmanabhan Gupshup Technology India Pvt Ltd Bengaluru India School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Dept of Computer Application School of Computing Science and Engineering Galgotias Univeristy India Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Alzheimer’s, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals’ independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer’s disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer’s disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD.

关键词： Surveys Accuracy Magnetic resonance imaging Predictive models Convolutional neural networks Alzheimer's disease Kernel

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