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Senti Comment: Event Success Prediction through Sentiment Analysis of User Feedback 5

Senti Comment: Event Success Prediction through Sentiment An...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Varma, Nadimpalli Madana Kailash Vidya, Rakapu Sri SriSaiteja, Thullimilli Teja, Chintakunta Pranay Manchala, Yugandhar Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Department of Computer Science & Engineering Vardhaman College of Engineering Telanagana Hyderabad501286 India Department of Information Technology Vardhaman College of Engineering Telangana Hyderabad501286 India

ISBN: (纸本)9798350361155

Sentiment analysis has emerged as a pivotal tool in extracting valuable insights from user-generated content, with its applications spanning various *** paper presents a novel approach to event evaluation using sentiment analysis techniques within the domain of Natural Language Processing (NLP). By analyzing user comments from diverse events such as parties, college fests, and conferences, we develop a robust NLP model to assess event success based on positive sentiment. Our model offers real-time insights into event reception and provides interpretative analysis of user feedback, including suggestions and expressions of appreciation. This research contributes to advancing sentiment analysis methodologies and equips event organizers with a valuable tool for enhancing future event planning strategies. © 2024 IEEE.

关键词： Data visualization

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Edge Computing Devices Authentication using Quantum Computing 15

Edge Computing Devices Authentication using Quantum Computin...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Telsang, Vinayak A. Kakkasageri, Mahabaleshwar S. Devangavi, Anil D. Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (纸本)9798350370249

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(VV), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices. ©2024 IEEE.

关键词： Authentication Blockchain Cryptography Edge computing Quantum key

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Understanding Resolution of Multi-Language Bugs: An Empirical Study on Apache Projects 17

Understanding Resolution of Multi-Language Bugs: An Empirica...

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17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2023

作者： Li, Zengyang Wang, Wenshuo Wang, Sicheng Liang, Peng Mo, Ran School of Computer Science Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9781665452236

Background: In modern software systems, more and more systems are written in multiple programming languages (PLs). There is no comprehensive investigation on the phenomenon of multi-programming-language (MPL) bugs, which resolution involves source files written in multiple PLs. Aim: This work investigated the characteristics of bug resolution in MPL software systems and explored the reasons why bug resolution involves multiple PLs. Method: We conducted an empirical study on 54 MPL projects selected from 655 Apache OSS projects, of which 66,932 bugs were analyzed. Results: (1) the percentage of MPL bugs (MPLBs) in the selected projects ranges from 0.17% to 42.26%, and the percentage of MPLBs for all projects as a whole is 10.01%;(2) 95.0% and 4.5% of all the MPLBs involve source files written in 2 and 3 PLs, respectively;(3) the change complexity resolution characteristics of MPLBs tend to be higher than those of single-programming-language bugs (SPLBs);(4) the open time for MPLBs is 19.52% to 529.57% significantly longer than SPLBs regarding 9 PL combinations;(5) the reopen rate of bugs involving the PL combination of JavaScript and Python reaches 20.66%;(6) we found 6 causes why the bug resolution involves multiple PLs and identified 5 cross-language calling mechanisms. Conclusion: MPLBs are related to increased development difficulty. © 2023 IEEE.

关键词： Open source software

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Forecasting of Photovoltaic Power with ARO based AI approach 2

Forecasting of Photovoltaic Power with ARO based AI approach

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Nalini, B.M. Khan, Zabiha Nayana, S. Achyutha, Prasad N. Manjula, G. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bgs College of Engineering and Technology Department of Cse Bangalore India

ISBN: (纸本)9798350382952

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. Systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Modeling and Analysis of Campus Placements Using machine learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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Smart Agriculture: A Comprehensive Approach to Crop Disease Diagnosis using Offline Multilingual Technologies

Smart Agriculture: A Comprehensive Approach to Crop Disease ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Ashok K Jimsha K Mattew Chandana K S Maharathi G N Abhilash GM Nithish Somani Computer Science and Engineering New Horizon College of Engineering Bengaluru Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research study presents a comprehensive multilingual approach to crop disease diagnosis aimed at transforming agricultural practices. The proposed system leverages advanced deep learning models such as ResNet, EfficientNet, and MobileNet to automate plant phenotyping using high-resolution images. Beyond disease identification, it provides intelligent plant parenting recommendations to enhance crop breeding strategies. The system is equipped with multilingual support and offline capabilities, ensuring global accessibility, especially in areas with limited internet access. Additionally, the model delivers outputs by detecting diseases and offering diagnosis in the user's native language, facilitating better decision-making for farmers. Experimental results from real-world scenarios demonstrate the system's high accuracy and potential to drive sustainable agriculture, accelerate genetic progress, and improve crop prediction.

关键词： Smart agriculture Deep learning Decision making Crops Genetics Multilingual Medical diagnosis Informatics Mobile computing Diseases

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Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis Solar Tracking System 4

Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis So...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Ravikumar, S. Eugene Berna, I. Vijay, K. Pradeep, R. Babu, R. R&d Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of College of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

The photovoltaic effect has permanently transformed the landscape of renewable sources by making it possible to convert solar energy into useable power. This concept is based on solar photovoltaic (PV) cells, but current PV technology limits us to capturing just a small percentage of the sun's energy at any given time. However, productivity can be greatly increased by using solar monitoring equipment. Creating and validating a low-priced active dual-axis solar tracking system is a breeze if you follow the steps outlined in this article. The best orientation for solar panels is perpendicular to the sun because their production is directly related to the amount of light they receive. Conventional solar panels have trouble keeping up with the ever-changing ideal angle for sunlight absorption and energy gathering. By tracing the sun's movement across the sky, solar trackers can triple the effectiveness of solar panels. This technological advancement is expected to lead to an improvement in energy generation and overall efficiency. This state-of-the-art solar tracker is powered by LEDs and an Arduino Uno. These systems work together to follow the sun's motion and maintain the solar panels facing the appropriate way. To ensure that the solar panels receive the most amount of sunshine possible, the system will automatically adjust their angle. Data processing and analysis in LabVIEW demonstrated the improved efficiency of the novel architecture. Researchers discovered a remarkable 13.44% boost in energy production compared to conventional fixed solar PV systems. © 2024 IEEE.

关键词： Solar panels

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learning to intervene on concept bottlenecks 24

Learning to intervene on concept bottlenecks

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Proceedings of the 41st International Conference on machine learning

作者： David Steinmann Wolfgang Stammer Felix Friedrich Kristian Kersting Artificial Intelligence and Machine Learning Group TU Darmstadt Germany Artificial Intelligence and Machine Learning Group TU Darmstadt Germany and Hessian Center for Artificial Intelligence (hessian.AI) Darmstadt Germany Artificial Intelligence and Machine Learning Group TU Darmstadt Germany and Hessian Center for Artificial Intelligence (hessian.AI) Darmstadt Germany and Centre for Cognitive Science TU Darmstadt Germany and German Center for Artificial Intelligence (DFKI).

While deep learning models often lack interpretability, concept bottleneck models (CBMs) provide inherent explanations via their concept representations. Moreover, they allow users to perform interventional interactions on these concepts by updating the concept values and thus correcting the predictive output of the model. Up to this point, these interventions were typically applied to the model just once and then discarded. To rectify this, we present concept bottleneck memory models (CB2Ms), which keep a memory of past interventions. Specifically, CB2Ms leverage a two-fold memory to generalize interventions to appropriate novel situations, enabling the model to identify errors and reapply previous interventions. This way, a CB2M learns to automatically improve model performance from a few initially obtained interventions. If no prior human interventions are available, a CB2M can detect potential mistakes of the CBM bottleneck and request targeted interventions. Our experimental evaluations on challenging scenarios like handling distribution shifts and confounded data demonstrate that CB2Ms are able to successfully generalize interventions to unseen data and can indeed identify wrongly inferred concepts. Hence, CB2Ms are a valuable tool for users to provide interactive feedback on CBMs, by guiding a user's interaction and requiring fewer interventions.

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A Classification of Face Recognition System Using Faster Region-Based Convolutional Neural Network 2

A Classification of Face Recognition System Using Faster Reg...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Maheswari, M. Alabdeli, Haider Aruna, T.M. Reddy, R. Archana Revathi, R. School of Computing Srm Institue of Science and Technology Department of Computational Intelligence Kattankulathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India School of Computer Science and Engineering Sr University Warangal India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult as the poses in the input images vary, various conditions of light, brightness, expressions, ages. To overcome from it, a proposed method was introduced in this paper known as Faster Region-based Convolutional Neural Network (RCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method was implemented on ORL database consisting of 40 classes, then the images in the database undergoes image resizing and enhancing in the preprocessing stage. Where, Image resizing id done to reduce images size so that every image is same in size that will minimize the processing time and image enhancing was done to adjust the brightness of the image. In further, for extracting the features from preprocessed images, CNN is used, so that extracted features are given to the classification stage to detect the face of human or not. In detecting the faces, proposed Faster RCNN achieved high accuracy of 98.64% when compared with other existing methods like IFC, EFRA and VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Track-before-detect algorithm based optimized interactive multiple model particle filtering for tracking maneuvering weak target

Track-before-detect algorithm based optimized interactive mu...

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2024 International Conference on Image Processing and artificial intelligence, ICIPAl 2024

作者： Wu, Xiaojia Yang, Jinlong School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China

ISBN: (纸本)9781510681514

This paper proposes an optimized algorithm for tracking maneuvering weak radar targets in complex sea environments. In the proposal, we first preprocesses radar echo images using the fractional Fourier transform (FrFT) to reduce clutter interference. Then, Feedback factor and residual resampling are introduced to optimize the interactive multiple model particle filtering (IMMPF) algorithm to achieve effective tracking of maneuvering targets. Additionally, the optimized IMMPF is combined with track before detect algorithm (TBD) to suppress clutter and accumulate target trajectory. Simulation experiment results demonstrate that the proposed algorithm can effectively track targets compared to other TBD algorithms. © 2024 SPIE.

关键词： Monte Carlo methods

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