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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vivekanand, Chettiyar Vani Kumar, P. Amizhthini, A. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. This study will propose a web application that uses advanced time series models to try and overcome such challenges. The system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models;Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis;Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting;Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets;and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. This solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. The goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model. © 2025 IEEE.

关键词： Ethereum

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Enhancing Farm Security System with AI-Power-Driven Animal Intrusion Detection Mechanism

Enhancing Farm Security System with AI-Power-Driven Animal I...

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Disruptive Technologies (ICDT), International Conference on

作者： Akshay Saxena Aniket Shisodia Divya Upadhyay Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Growing need for effective farm security solutions drives the development of the “Animal Intrusion Detection in Farms” system. This paper aims to identify and inform farmers about animal invasions in order to increase agricultural area security. Making use of OpenCV, the system uses computer vision techniques to identify animals crossing the farm boundaries. Setting alarms should a trespass be discovered since the system scans images to identify animals using real-time video data acquired by cameras all around the farm. Then, supplied these alarms via notifications, farm owners may react quickly. By automating the monitoring process, the method drastically reduces human participation and protects valuable crops and cattle against animal damage. The solution helps to improve farm security by combining real-time notifications, automated detection, and accurate animal recognition. Modern farm security focusses on OpenCV and deep learning models since they promise significant accuracy in detection, even under various environmental conditions, thereby offering a consistent and scalable solution.

关键词： YOLO computer vision Accuracy Animals Intrusion detection Crops Cows Real-time systems Security Convolutional neural networks

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Event-based nonsingular fixed-time containment control for nonlinear multiagent systems with dynamic uncertainties

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science China(Information sciences) 2025年第5期68卷 413-414页

作者： Yuanbo SU Qihe SHAN Tieshan LI C.L.Philip CHEN Navigation College Dalian Maritime University School of Automation Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering South China University of Technology

Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.

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Facial Keypoint Detection using a Modified Convolutional Neural Network with RESNET50 2

Facial Keypoint Detection using a Modified Convolutional Neu...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Verma, Sonia Singhal, Pooja Gupta, Ritu Singh, Abhilasha Kumar, Arun Abes Engineering College Department of Computer Science Uttar Pradesh Ghaziabad201009 India Banasthali University Department of Computer Science Rajasthan India Bhagwan Parshuram Institute of Technology Ggsipu Department of Computer Science and Engineering Delhi India Srm Institute of Science and Technology Department of Computer Science and Engineering Delhi-NCR Campus Ghaziabad India

ISBN: (纸本)9798350390179

This study introduces an attentional convolutional network for facial expression recognition, highlighting the need of facial area focus for accurate emotion detection. By using this strategy, hybrid neural networks with ResNet50 architecture show promising f advances. Visualization accentuates face traits important for detecting emotions. Using transfer learning and fine-tuning, the hybrid ResNet50 architecture collects important face features to improve emotion recognition and generalization. Although Applications such as facial recognition and emotion tracking require accurate facial keypoint detection in order to function properly. The purpose of this research is to present an improved method that makes use of a modified convolutional neural network (CNN) that is constructed on the RESNET50 architecture. Enhancing RESNET50 with particular changes allows our method to increase both the extraction of features and the localization of keypoints respectively. Typical problems that are seen in conventional CNNs are addressed by the model that has been suggested. These problems include restricted feature representation and generalization concerns. A number of exhaustive studies have demonstrated that our method delivers a greater level of accuracy and robustness in facial keypoint detection when compared to the leading methods that are currently in use. With this new development, real applications now have access to a solution that is both more effective and more scalable. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Portable Device for Trekking People in Forest 2

Smart Portable Device for Trekking People in Forest

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gowri, B. Shyamala Rajalakshmi, B. Rohith, R.V. Roshan, P. Ram Prasath, V. Easwari Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350387933

The increasing concern regarding animal attacks within rural communities and among forestry workers highlights the pressing need for effective wild animal tracking systems. Surveillance cameras and drones have become standard tools for monitoring in response to this challenge. However, ensuring the safety of individuals in these areas still requires precise location data, tracking of animal movements, and accurate animal detection. While traditional computer vision and machine learning techniques have shown promise, their practical implementation has been hindered by their cost and complexity. To address this, this study introduces an innovative approach: a hybrid model that combines the Visual Geometry Group (VGG)-19 architecture with bidirectional long short-term memory (Bi-LSTM) networks. Beyond animal identification, this approach generates real-time alerts based on animal behavior, swiftly transmitting them to the nearby forest office via SMS for immediate action. By integrating these systems, the risk of wildlife encounters is reduced, and safety measures in forested regions are significantly enhanced. Importantly, the hybrid model exhibits outstanding real-time data processing capabilities with remarkable performance metrics, including an impressive accuracy of 98%, a mAP of 77%, and an exceptional FPS rate of 170. Rigorous testing was conducted using a dataset comprising 40,000 images from 25 distinct animal classes, consistently demonstrating precision and accuracy levels exceeding 97.9%. In summary, the 'hybrid VGG-19 and Bi-LSTM model' offers a reliable and efficient solution for addressing animal attacks, safeguarding lives, and enabling rapid communication with local authorities, ultimately improving safety and minimizing the harm caused by encounters with wildlife. © 2024 IEEE.

关键词： Cameras

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Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

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computers, Materials & Continua 2024年第6期79卷 4603-4620页

作者： Mohammad Aldossary Hatem A.Alharbi Nasir Ayub Department of Computer Engineering and Information College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11991Saudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer ***,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy *** paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research *** IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data *** sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for *** data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center *** the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT *** model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power ***,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and *** NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark *** findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive

关键词： Cloud computing energy efficiency data center optimization internet of things(IoT) hybrid models

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Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

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Intelligent Automation & Soft Computing 2023年第3期35卷 2893-2907页

作者： C.Santhosh Kumar K.Vishnu Kumar Department of Computer Science and Engineering Priyadarshini Engineering College635751TamilnaduIndia Department of Computer Science and Engineering KPR Institute of Engineering&TechnologyCoimbatore641407India

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to ofﬂoading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and conﬁdentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational ofﬂoading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

关键词： Peer-to-peer computing energy and delay aware ofﬂoading edge-cloud enabled healthcare system parkinson’s disease prediction

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IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

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computers, Materials & Continua 2023年第7期76卷 1027-1045页

作者： Sharad Saxena Shailendra Mishra Mohammed Baljon Shamiksha Mishra Sunil Kumar Sharma Prakhar Goel Shubham Gupta Vinay Kishore Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjab147001India Department of Computer Engineering College of Computer&Information ScienceMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Engineering National Institute of TechnologyJamshedpur831001India

In recent years,the growth of female employees in the commercial market and industries has *** a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s *** exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and *** the time of the attack,it appears the women were immobilized and needed immediate *** self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or *** proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud *** is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud *** can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location *** has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 *** average activation time is 2.079 s.

关键词： Perpetrator safety IoT women security GPS tracking GSM

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Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

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Intelligent Automation & Soft Computing 2023年第4期36卷 183-192页

作者： S.Samsudeen Shaffi I.Muthulakshmi Department of Computer Science and Engineering PET Engineering CollegeVallioorTamil Nadu627117India Department of Computer Science and Engineering V.V.College of EngineeringTisaiyanvilaiTamil Nadu627657India

As data grows in size,search engines face new challenges in extracting more relevant content for users’*** a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s ***,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate *** a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher *** paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various *** Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences ***ﬁndings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankﬁnal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

关键词： Weighted pagerank algorithms search engines web pages web crawlers World Wide Web

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An Advanced Brain Tumour Detection Framework Using Improved Fast Mask R-CNN Techniques for Enhanced Accuracy

An Advanced Brain Tumour Detection Framework Using Improved ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shankar, G. Kumar, V.R. Abhishek Srikanth, Akkaru Reddy, Thummala Jathin Sahil, Shaik Rmd Engineering College Department of Computer Science and Engineering India Rmd Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image processing algorithms. Identification of brain tumors is still a significant difficulty in medical imaging since successful therapy depends on early and accurate identification. By precisely identifying tumor boundaries in brain MRI scans, the proposed model makes use of an enhanced version of FMRCNN is intended to enhance separating and categorizing performance. Sophisticated image processing methods are used to improve the extraction of features, allowing the model to more accurately differentiate between tumor and non-tumor areas. By lowering false positives and true negatives, the structure seeks to maintain computing power while greatly increasing detection rates. The proposed strategy outperforms conventional techniques in terms of reliability, specificity, and sensitivity, according to the results of experiments, which makes it a viable tool for support with clinical choices as well as initial diagnosis in neuroimaging. IFRCNN was applied using the Open CV package in conjunction with an SVM algorithm. Using 218 photos as an initial training set, this study's approach demonstrated a 100% success rate in classifying meningiomas and 87.5% accuracy in classifying gliomas, with an average conviction score of 94.6% in the segments of meningioma tumors. A neurologist does a manual examination and ground truth assessment to verify the divided tumor areas. © 2024 IEEE.

关键词： Convolutional neural networks

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