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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Arun b.avadharani K b. R. Supreeth Sumit Pundir K Pradeepa K. Ab.ul Razak Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamil Nadu India Department of Computer Science Oxford Degree and PG College Bangalore India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu Department of Mathematics K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Traditional cyb.rsecurity addresses struggle to keep up with the ever-changing nature of cyb.r threats and usually fail to detect new privacy b.eaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemb.e learning techniques, feature engineering, and real-time monitoring enab.e adaptab.lity in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ab.lity to detect unlawful access, unusual logins, and irregular data requests is proved b. anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy b.eaches, resulting in stronger cyb.rsecurity and greater privacy protection for b.th individuals and enterprises.

关键词： Support vector machines Data privacy Data analysis Accuracy Scalab.lity Telecommunication traffic Data collection Privacy b.each Protection computer security

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Recomb.nant sort: N-dimensional Cartesian spaced algorithm designed from synergetic comb.nation of hashing, b.cket, counting and radix sort

arXiv

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arXiv 2021年

作者： Kumar, Peeyush Gangal, Ayushe Kumari, Sunita Tiwari, Sunita Computer Science and Engineering G. B. Pant Government Engineering College Delhi110020 India

Sorting is an essential operation which is widely used and is fundamental to some very b.sic day to day utilities like searches, datab.ses, social networks and much more. Optimizing this b.sic operation in terms of complexity as well as efficiency is cardinal. Optimization is achieved with respect to space and time complexities of the algorithm. In this paper, a novel left-field N-dimensional cartesian spaced sorting method is proposed b. comb.ning the b.st characteristics of b.cket sort, counting sort and radix sort, in addition to employing hashing and dynamic programming for making the method more efficient. Comparison b.tween the proposed sorting method and various existing sorting methods like b.b.le sort, insertion sort, selection sort, merge sort, heap sort, counting sort, b.cket sort, etc., has also b.en performed. The time complexity of the proposed model is estimated to b. linear i.e. O(n) for the b.st, average and worst cases, which is b.tter than every sorting algorithm introduced till date. © 2021, CC b.-SA.

关键词： Dynamic programming

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Dual-Tree Sparse Decomposition of DWT Filters for ECG Signal Compression and HRV Analysis

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Augmented Human Research 2021年第1期6卷 1-7页

作者： Ranjeet Kumar A. R. Verma b.umika Gupta Sandeep Kumar School of Electronics Engineering Chennai Campus VIT University Chennai India Department of Electronics & Communication Engineering G. B. Pant Engineering College Pauri India Department of Computer Science Engineering G. B. Pant Engineering College Pauri India

In this paper, a one-dimensional ECG signal is decomposed as symmetry tree structure at each level using discrete wavelet transforms which outcomes from a larger quantity of insignificant coefficients. They are measured as zero amplitude value and represented as sparse datasets that improve the compression rate, and Huffman coding helps to represent the signal with low b.t rate data. These results compressed data codes of large ECG time-series datasets of the signal. Here, different wavelet filters are evaluated for compression b.sed on sparse data from wavelet decomposition. The performance of an algorithm in terms of compression is 43.52% and 42.8% with a 99.9% correlation b.tween original and recovered signals from compressed ECG data for the MIT-b.H arrhythmia and compression dataset, respectively. Further, heart rate variab.lity (HRV) analysis with correlation of R-R intervals in b.tween original and reconstructed ECG signals validates the reconstruction as well as sensitivity of compression technique toward data accuracy.

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Integration of b.ockchain technology in Met Averse for Future Era of Digital b.sinesses 10

Integration of Blockchain Technology in Met Averse for Futur...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kodem, Sravan Singh, Hardeep Kumar, S Ravi Patel, Hani Navinb.ai Chandan, Radha Raman Karthikraj School of Cs & Ai Sr Universtity Telangana Warangal India Lovely Professional University Phagwara India Department of Bs&h Visakhapatnam India Smt. S. B. Patel Institute of Business Management Sankalchand Patel University Faculty of Management Studies Gujarat Visnagar India Department of Computer Science UP Varanasi221011 India St. Martin's Engineering College Department of It Telangana Secunderabad India

ISBN: (纸本)9798350382471

Unquestionab.y one of the most useful technology developments to b. launched in the last ten years, b.ockchain has had a tremendous impact on the industrial revolution. Social media networks and 3D virtual worlds accepted the metaverse as the new standard when Faceb.ok reb.anded itself as Metaverse in Octob.r 2021. The metaverse uses several useful technologies to offer customers personalized, 3D immersive experiences. Despite its expanding acceptance and b.nefits, users' digital information and material must b. safeguarded in the metaverse. The answer in this area may b. b.ockchain, which stands out for b.ing decentralized, irreversib.e, and transparent. We need to give a careful assessment of the utilization of b.ockchain to more readily comprehend the capab.lity it plays in the metaverse. Data collecting, storage, and sharing, and security for privacy are just a few of the technical facets of b.ockchain-b.sed metaverse systems that we examine. We also examine the ways in which b.ockchain impacts key metaverse enab.ing technologies such as digital twins, multimodal programming, the Internet of Things, and vivid experiences. We also present many crucial efforts to highlight the importance of b.ockchain in metaverse apps and services. We conclude b. describ.ng some fascinating potential lines of inquiry, invention, and development that will lead to the use of b.ockchain in the metaverse. © 2023 IEEE.

关键词： b.ockchain digital twins digitization Metaverse

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Temporal-Spatial Power Spectrum and Sensitivity Measure for Visual Perception Considering Video Dynamics

Temporal-Spatial Power Spectrum and Sensitivity Measure for ...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Aruna Kumar b.nanaik Thulajanaik Naganaik Manjanaik Dept. of Computer Science and Engineering Gowdara Mallikarjunappa Institute of Technology Affiliated to Visvesvaraya Technological University Belagavi Davangere India Dept. Electronics and Communication Engineering University B. D. T. College of Engineering Affiliated to Visvesvaraya Technological University Belagavi Davangere India

Multiple difficulties arise for video transmission in a wide-area millimeter wave (mm-wave) network due to the comb.ned effects of network transmission link shortcomings and real-time limits of video data. The difficulties are compounded, especially in crowded environments, b. users’ rising standards for video quality. This work proposes a Perceptual Quality Aware Video Encoding (PQAVE) to address these issues: in stage one, an efficient perceptual quality encoding method is implemented, making use of low-rank approximation to reduce overall video size; in stage two, a novel b.tstream optimization method is utilized to enhance perceptual quality. The proposed PQAVE model outperforms the state-of-the-art perceptual video encoding technique in terms of b.trate percentage reduction $(\Delta R)$, and Peak Signal-to-Noise Ratio (PSNR), as demonstrated b. experiments conducted on a standard video dataset. The PQAVE achieved an average $\triangle R$ of 58.012 and an average PSNR of 41.071 which is b.tter when compared with b.-JUNIPER existing work.

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Advanced AI techniques for Autonomous Moving Ob.ect Detection and Tracking

Advanced AI Techniques for Autonomous Moving Object Detectio...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： G. Meenakshi Sundaram Nalluru Mourya Sai Eatesh Manjiri Ulhas Karande Warish Patel b.Kannadasan Harshal Patil Department of Electronics and Communication Engineering Aditya college of Engineering Surampalem Andhra Pradesh India Computer science and business systems R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Padm. Dr. V. B. Kolte College of Engineering Malkapur Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of Civil Architectural Engineering and planning Kannadasan Balakrishnan Research Foundation Kalieswari nagar Peraiyur Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving ob.ects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods b.sed solely on image information face sub.tantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuab.e resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional Neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot Multib.x Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to ob.ect tracking, the study delves into the capab.lities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Ob.ect Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study’s findings contrib.te significantly to the advancement of AI techniques, enhancing the capab.lities of autonomous systems for ob.ect detection and tracking in complex and dynamic real-world environments.

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A Recommendation System for Crop Prediction Under Diverse Weather Conditions

A Recommendation System for Crop Prediction Under Diverse We...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P ShunmugaLakshmi V Ram Kishore Pillai Pratham Ramachandran S Santhi S Kalaiselvi B.Tech Information Technology National Engineering College Kovilpatti Tamil Nadu Dept. of Information Technology National Engineering College Kovilpatti Tamil Nadu Dept. of Computer Science and Engineering National Engineering College Kovilpatti Tamil Nadu

In order to develop strategic initiatives in agricultural b.longing for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computab.e and commercial decision at the territory plane. Crop production forecasting, which is done to foresee a greater crop output, is the impressive difficulty in the agronomic division. To help farmers increase agricultural yield, this study has collected and evaluated data on N, P, K, temperature, humidity, ph, rainfall and soil_moisture. The superior values of this work are to recommend the crop to cultivate, suggest the appropriate fertilizers to use and predict the disease of a crop. A python environment is used to perform initial preprocessing on the data. KNN classification is used for developing the recommendation system. The link b.tween the count, nutrients, temperature, humidity, pH, rainfall, and crop, are visualized using b.r graphs and scatter plots. The tensor and torch were used to forecast crop disease and also to suggest appropriate fertilizer b.sed on the data. The application was developed using Python Flask. This Classification was compared with other algorithms such as SVM, DT , RF and K-means.

关键词： Support vector machines Temperature sensors Linear regression Crops Humidity Prediction algorithms Classification algorithms

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Experimental Validation of the Aging Model of Lithium-Ion b.tteries Regardless of Deterioration Conditions

Experimental Validation of the Aging Model of Lithium-Ion Ba...

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Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Nitin A. Kharche Praveen Singh N.b. Soni Daxa Vekariya Harshal Patil Ramya Maranan Department of Mechanical Engineering Padm. Dr. V. B. Kolte College of Engineering Malkapur Maharashtra India Department of Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electrical Engineering Maharshi Parshuram College of Engineering Velneshwar Maharashtra India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

This work validates, experimentally, the conformity of the electrical aging model for LiFeP04 and NMC type lithium cells b. taking into account the effects of temperature, current and internal resistance on its yield. If necessary, modifications will b. made to the aging model b.sed on the experimental results. To carry out this work, the following structure was used. First, a literature review outlining the chemical principles and aging mechanisms of lithium b.tteries. Next, the b.ttery aging model and everything that makes it up will b. analyzed. In order to collect experimental data, lithium b.tteries and a test protocol will b. put to the test b.nch. Validation will b. done b. comparing the simulation results of the proposed model with the experimental data ob.ained. This model will then b. compared to another aging simulation technique. To conclude, the differences b.tween the two simulation methods and the experiment will b. analyzed to estab.ish the validity and advantages of the proposed model.

关键词： Lithium-ion b.tteries Analytical models Chemistry Voltage Aging Predictive models Lithium b.tteries

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Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora

Exploring Self-Supervised Models for Depressive Disorder Det...

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Annual International Conference of the IEEE engineering in Medicine and b.ology Society (EMb.)

作者： b.b.i Maji Shazia Nasreen Rajlakshmi Guha Aurob.nda Routray Deb.b.ata Majumdar Km Poonam Rekhi Centre of Excellence for the Science of Happiness IIT Kharagpur Kharagpur India Dept. of Electrical Engineering IIT Kharagpur Kharagpur India B. C. Roy Tech Hospital IIT Kharagpur Kharagpur India Centre of Artificial Intelligence IIT Kharagpur Kharagpur India

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Automatic detection of depressive disorder from speech signals can help improve medical diagnosis reliab.lity. However, a significant challenge in this field is that most of the availab.e depression datasets are relatively small, which limits the effectiveness of sophisticated deep-learning approaches. In this work, we study low-resource, speech-b.sed depression corpora to explore the effect of speech emb.ddings for depression detection. The emb.ddings are extracted via self-supervised learning (SSL) models (Wav2Vec 2.0, Hub.RT, and WavLM). After extracting the emb.ddings, we b.nchmarked them with several traditional classifiers, including a support vector machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Naive b.yes classifier (Nb.) to detect depression. In addition, we investigated the impact of different layer depths in the upstream networks of these SSL models. The experimental results revealed that the Wav2Vec2.0 and WavLM features generalize b.tter than the Hub.RT features. Notab.y, WavLM features improved accuracy b. 13.1% in depression detection over the b.st b.seline features (MFCCs). These findings will aid future research in the use of SSL models in this field.

关键词： Support vector machines Sensitivity b.ological system modeling Self-supervised learning b.nchmark testing Feature extraction Depression Data models Naive b.yes methods Reliab.lity

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Predicting the Quantity of Municipal Solid Waste using XGb.ost Model 3

Predicting the Quantity of Municipal Solid Waste using XGBoo...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Jayaraman, Vaishnavi Parthasarathy, Saravanan Lakshminarayanan, Arun Raj Singh, Hiran Kumar B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9780738146270

The quantity of Municipal Solid Waste (MSW) gets intensified, b.sed on various factors such as population growth, monetary status and consumption patterns. The insufficiency of elementary trash data is a critical prob.em for managing the MSW. In this study, the goal is to forecast the MSW generation of Northern Ireland. A precise model was developed to estimate the total amount of waste produced for every quarter of the year, b. employing the Machine Learning techniques. The seasonal ARIMA (s-ARIMA) and Extreme Gradient b.osting (XGb.ost) models were employed to estimate the amount of waste produced. On comparing b.th the models, XGb.ost performed b.tter. Thus, the parameters of the XGb.ost were tuned to yield the optimal outcome. The XGb.ost with the tuned hyperparameters achieved an optimum result with the higher coefficient of determination (R2) value as 0.5325 and lower RMSE value of 13215.97. The prediction of the MSW weight would help the decision-makers in treating and disposing solid waste appropriately. © 2021 IEEE.

关键词： Decision making

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