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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Dinesh Kumar, K. Manikandan, K. Raja, S. Edwin Prabhu Shankar, B. Ravi, Jayavadivel Steena, T. Beni Vel Tech University Department of Computer Science and Engineering Chennai India Amet University Department of Eee Chennai India Presidency University School of Computer Science and Engineering Bengaluru India Kongunadu College of Engineering and Technology Department of Ece Chennai India

ISBN: (纸本)9798331508432

Increased rates of phishing attacks are one of the threats present in the increasingly connected society of the modern world especially for the youths. In this case, an ML techniques that involves patterns and characteristics to alert users of suspicious links and prevent them from being at risk, such as Naive Bayes, provide solutions. It also offers fairly good protection against the last kind of phishing exploits that employ misleading URLs as their implement, although the URLs are classified as being of either a malicious or a legitimate nature with the help of the Naive Bayes method. The primary purpose of the proposed work is to design a protective shield against phishing attacks to minimize people's exposure to such scams. The aim of the end applications is to build systems that are capable of capturing and blacklisting the dangerous links on their own to make the users safe when they are on the internet. The approach uses a novel machine learning algorithm which has been trained on several sets of real and fake URLs. It further analyzes the URL pattern, language used according to its database and prior defined pat terns in order ''to judge which connections are bad/worst and which are allowed/good.'' The above model is undoubtedly going to be elastic under all variations in the phishing strategies at the hands of a perfectly chosen training set. Essentially the program integrates with users' browsers, constantly analyzing URLs and notifying users promptly in the event that possible risks are detected. By enhancing users' confidence on their online interaction through the dependability of the application in identifying threats as evidenced in actual simulations based on different types of phishing threats. To ensure great safety with the network and safe data storage, the usage of the machine learning for malware identification and fake web link detection is required. This is in a way underlines how people might be prepared for how to go about their business o

关键词： Phishing

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Forecasting Stock Market Volatility Using XGBoost: A Time Series Analysis 3

Forecasting Stock Market Volatility Using XGBoost: A Time Se...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sharma, Tripti Prasad, Sanjeev Kumar Prasad, Shreeya Verma, Indu Sharma, Anupama Ghaziabad India School of Computer Science Engineering Galgotias University Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India University Delhi-NCR India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331528201

Forecasting stock market volatility presents significant challenges and opportunities for both practitioners and researchers in the financial sector. This paper explores the application of eXtreme Gradient Boosting (XGBoost), an advanced ensemble machine learning technique, to predict stock market volatility using time series data from the National Stock Exchange of India (NSEI). Our methodology utilizes past price data containing open, high, low, and close prices, along with capacity and adjusted close values, to capture the underlying patterns indicative of future market movements. We preprocess this data using techniques like normalization and feature engineering to enhance the predictive capabilities of our model. The XGBoost model is upgraded through extensive hyperparameter tuning and is considered in contrast to customary measurements like Mean Squared Error (MSE), Root Mean Squared Blunder (RMSE), and the R2 Score to survey its precision and adequacy. Our results demonstrate that XGBoost can effectively capture the complexities and non-linear relationships inherent in stock price movements, outperforming standard benchmark models. The findings suggest that machine learning models, particularly those based on tree ensemble methods like XGBoost, provide a robust tool for financial analysts seeking to mitigate risks and capture opportunities in volatile markets. This paper contributes to the ongoing discourse in financial econometrics by confirming the viability of advanced machine learning techniques in forecasting market dynamics, thus offering valuable insights for both theoretical advancement and practical application in financial forecasting. © 2024 IEEE.

关键词： Time series

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Image Caption using CNN in computer Vision

Image Caption using CNN in Computer Vision

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kumar, Rohit Goel, Gaurav Chandigarh Group of College Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350322309

Programmatic captioning is the system of making captions or textual content primarily based totally on picture content material. This is an AI challenge that consists of each everyday speech processing (producing textual content) and PC vision (information picture content material). Program captioning is presently a completely lagging and evolving examination topic. Different new techniques are discovered little by little to obtain perfect outcomes on this area. Still, it takes a number of price to get human-equal outcomes. This studies is supposed to discover precise and present day strategies and fashions used for imaging with deep mastering in a particular manner. What strategies are carried out to apply those fashions, and what strategies provide right outcomes? To this end, we performed an green paper seek of late-level research from 2017 to 2022 from noteworthy datasets (Scopus, Web of sciences, IEEEXplore). A overall of sixty one considerable research had been observed which can be applicable to the factors on this study. It seems that CNN is used to recognize picture content material and hit upon gadgets in images, even as RNN or LSTM is used withinside the age of languages. The maximum normally used datasets are MS COCO, utilized in all tests, and Glimmer 8k and Glint 30k. The maximum normally used grading framework is BLEU (1-4) that's utilized in all grading. It became additionally cited that LSTM with CNN beat RNN with CNN. We observed that the 2 maximum promising strategies for going for walks this version are encoder-decoders and attention tools. Combining those can power huge outcomes. This studies gives course and concept for researchers thinking about including programmed closed captioning. © 2023 IEEE.

关键词： Grading

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HDL Environment for the Synthesis of 2-Dimensional and 3- Dimensional Network on Chip Mesh Router Architecture

HDL Environment for the Synthesis of 2-Dimensional and 3- Di...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kumari, Shweta Rajput, Kapil Singh, Gurwinder Jain, Arpit Sachi, Savya Manwal, Manika Shri Fanishwar Nath Renu Engineering College Department Of Computer Science & Engineering Bihar Araria India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Department Of Aitcse Punjab Mohali India Koneru Lakshmaiah Education Foundation Department Of Computer Science & Engineering AP Vaddeswaram India L N Mishra College Of Business Management Department Of Information Technology Bihar Muzaffarpur India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Application specific Network on Chip (NoC) designs are quickly becoming the technology of choice for solving the problem of multiprocessor system architecture. Broadband, interposes communication, deadlock avoidance, port bandwidth, router architecture, and bespoke NoCs may all be improved by modelling and simulation of multilayer network structures and synthesis. Topology-based organization of 2D and 3D routers allows for the construction and laDyout of NoCs. The articles compare and contrast the design and analysis of two-dimensional and three-dimensional network-on-chip routers for setting up mesh topological nodes in a network. Xilinx 14.2 is used to execute the code. The most recent version of modalism, 10.0 student edition, is used to functionally mimic the program and modules. The experimental work and thorough testing on the Virtex-5 FPGA confirm the chip design. © 2024 IEEE.

关键词： Network-on-chip

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Loan Status Prediction in the Banking Sector using Machine Learning

Loan Status Prediction in the Banking Sector using Machine L...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Kusumlata Bhatt Pranchal Sharma Megha Verma Kadambri Agarwal Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

There have been tremendous advancements in technology that have led to an increase in the needs of people. This, in turn, has led to more loan approval requests in the banking sector. Some attributes are considered to check loan status while selecting an applicant for loan approval. Banks are facing a serious challenge when it comes to evaluating loan applications and mitigating the risks associated with borrowers potentially defaulting on their loans. This process is particularly laborious for banks, as they must thoroughly verify each individual's loan eligibility. This paper proposes utilizing machine learning models with Ensemble Learning Techniques to determine the viability of granting individual loan requests. By using this approach, it is possible to enhance the accuracy with which suitable candidates are selected from an existing list. Therefore, this process can be used to address the aforementioned concerns surrounding loan approval processes. The model is helpful to both bank staff and applicants as it drastically reduces the time taken to sanction the loan.

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Attention based 1-D CNN system for detection of Freezing of gait (FOG) in Parkinsons Patients 15

Attention based 1-D CNN system for detection of Freezing of ...

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

作者： Pradeep Kumar, D. Sowmya, B.J. Vincent, Ashvin Pandiyan, Aravindh Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India Bangalore India

ISBN: (纸本)9798350370249

One common clinical symptom seen in Parkinson's disease (PD) patients is freezing of gait (FOG). It manifests as an irregular gait, marked by abrupt, involuntary stopping of movement during gait episodes. FOG entails a major negative effect on PD patients' quality of life. In this work, we analyze the movement data that is collected using the lower-back 3D accelerometer worn by the patients during FOG episodes. By employing machine learning methods to classify this data, we aim to accurately detect FOG trends. In particular, we focus on Deep Learning-based techniques, specifically an attention-based 1D Convolutional Neural Network (1D-CNN), due to their capacity to extract intricate features from unprocessed sensor input. Our results indicate that the proposed model achieves AUC scores of 0.9032 with respect to the TDCSFOG dataset and 0.8682 for the DeFOG dataset, for a combined average AUC of 0.8857. Mean Average Precision (mAP) scores for TDCSFOG and DeFOG reach 0.8388 and 0.779, respectively, for an average of 0.8089, showing the ability of the model to predict FOG occurrences over time. These findings suggest techniques based on Deep Learning enhance FOG detection, allowing clinicians to more effectively tailor treatments and care strategies for Parkinson's patients, thus enhancing their ability to move and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Classifiers Based on Spam data in Twitter Sentiments 2nd

Comparative Analysis of Classifiers Based on Spam Data in Tw...

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2nd International Conference on Machine Learning, Advances in Computing,Renewable Energy and communication, MARC 2020

作者： Kumar, Santosh Kumar, Ravi Sidhu Computer Science & Engineering Department ABES Engineering College Ghaziabad UP201009 India Shri Venkateshwara University Gajraula UP India

ISBN: (纸本)9789811623530

In the present scenario, sentiment analysis has gained much attention in the field of text mining. As social media have a huge impact on one’s life, people use social media as a tool to express their feelings, thoughts, opinions, emotions and ideology. With the help of sentiment analysis, we can provide computational treatment for sentiments, opinions and subjectivity of text. Detecting spams from various social media sites is a challenging task as the messages contain the short informal text. In this paper, we have tested different classifiers on spam data of users’ tweets based on spammer and non-spammers. The classifiers used for the purpose are Naives Bayes, Simple Logistic, J48 pruned tree, Bayes network classifier and Random Forest. Analysis results showed that random forest is proving the highest accuracy among all the algorithms under consideration. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

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Intelligent Medicine 2024年第1期4卷 43-51页

作者： Shivangi Raghav Aastha Suri Deepika Kumar Aakansha Aakansha Muskan Rathore Sudipta Roy Department of Computer Science&Engineering Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia Artificial Intelligence&Data Science Jio InstituteNavi Mumbai410206India

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human *** molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients *** have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient *** This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional *** proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier *** hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature *** performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature *** model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression *** This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

关键词： Machine learning Colorectal cancer Feature selection Classification Clustering

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