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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Megha Pandey Vinay Kumar S B Jatin Khurana B. Kalpana Kishor R. Pathak Mukesh Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India −1 Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications.

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Method for Recognizing Hand Gestures in Real- Time Across Multiple Sensors Using machine learning

Method for Recognizing Hand Gestures in Real- Time Across Mu...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Jayanthi M Rajeswari. S Department of Computer Science and Engineering Kalaignar Karunanidhi Institute of Technology Coimbatore India

The field of human-computer interaction includes multimodal gesture detection as a key task. In human-machine interface, wearable sensors for hand gestures consume additional potential than ambient detection due to their short price, weightiness, and wide applicability. Despite the fact that research on hand motions with wearable sensors already exists, the results are rarely transferable from one scenario to another. And when it comes to collaboration between humans and technology, there is still no agreed-upon protocol for hand signals. In this study, we introduce a new technique of using hand gestures to command the environment. An affordable Internet of Things (IoT) sensor-pluggable device that has an integrated accelerometer and gyroscope is used to collect the data for analysis. Inputs to the feature extractor network include the raw data from the RGB sense modality, the visual flow sense modality, and the disassembled information from the hands. This work provides an additional modality, feature extraction based on temporal differences in RGB sense modality. The characteristics of these modes are learned using a pre-trained version of the Googlenet Caffe model. All of these components need to come together as a whole for the fusion procedure to be successful. When doing a classification, the bidirectional LSTM network is used. The new approach is put through its paces using the IPN Hand dataset. When first starting out, each modality is employed independently. Then, we employ such mixtures to boost classification precision even further. Combining the segmented indicator sense modality with the optical movement mode and the chronological transformation of the RGB sense modality yielded the greatest results. The suggested method for multimodal gesture recognition outperforms the benchmark methods, as shown by the simulation results

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Crime Detection from Pre-Crime Video Analysis with Augmented Activity Information

Crime Detection from Pre-Crime Video Analysis with Augmented...

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Computing and machine Intelligence (ICMI), international conference on

作者： Sedat Kilic Mihran Tuceryan Department of Computer and Information Science Indiana University - Purdue University - Indianapolis Indianapolis Indiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This study focuses on the detection of pre-crime events in videos, specifically shoplifting. In video understanding, visual features, pose, and emotion information are inherently frame-centric, providing insights in understanding within isolated frames. Our approach introduces a novel method by extracting activity information between frames and incorporating visual features augmented with human pose and emotion information to enhance the understanding of pre-shoplifting behaviors. We used a set of CCTV videos in which at the end of some clips the customers shoplift and in others they do not. We used these videos as training data with a transformer machine learning architecture. We augmented low level video analysis with pose, and emotion information on frame level and extracted activity information between frames. We conducted a systematic series of experiments to assess the performance of our model under two distinct configurations. The first configuration involved frame-centric visual features augmented with pose and emotion information, while the second configuration focused on activity information between frames. The results demonstrate improvements, underscoring the pivotal role of extracting temporal dynamics between frames in capturing nuanced activities leading up to the crime. The comprehension and capture of these activity patterns between frames proved to be crucial for a more thorough examination of pre-shoplifting events. The essence of our study's contribution is hinged upon the utilization of extracting temporal dynamics between frames, enabling the capture of crucial activities of individuals prior to engaging in shoplifting.

关键词： Visualization Systematics Training data machine learning Feature extraction Transformers data mining

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The mining Algorithm of Association Rules is Comprehensively Evaluated to Improve the Quality of Talent Training

The Mining Algorithm of Association Rules is Comprehensively...

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Advancement in Technology (ICONAT), international conference for

作者： Chen Xin QiGao Department of Management and Economics JingDeZhen Ceramic University jingdezhen Jiangxi China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Cultivating intelligence is an important part of the process of data analysis and research. Therefore, it is necessary to conduct a comprehensive evaluation of human resource *** talent training quality research is critical in the comprehensive evaluation of undergraduate majors to improve talents, however it has an issue with erroneous performance positioning. The typical Digital mining algorithm is unable to address the talent training issue in the comprehensive evaluation of undergraduate majors to improve talents, and the result is insufficient. As a result, a Association rule mining algorithm-based the Comprehensive Evaluation of Undergraduate Majors to Improve the Quality of Talent Training is provided, and the the Comprehensive Evaluation of Undergraduate Majors to Improve the Quality of Talent Training is assessed. To begin, the association rule theory is used to discover the influencing elements, and the indicators are split based on the talent training quality research’s needs to decrease interference factors in the talent training quality research. The association rule theory is then used to create a Association rule mining algorithm talent training quality research scheme, and the outcomes of the talent training quality research are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Association rule mining algorithm outperforms the standard Digital mining algorithm in terms of talent training quality research accuracy and time of influencing variables. The results show that the analysis of intelligent methods is needed to improve the recognition accuracy of talent training. Therefore, the results of the study show that the accuracy of talent identification can be improved by between 10% and 15%.

关键词： Training Accuracy data analysis Simulation Interference Association rule learning Internet Reliability MATLAB Standards

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Drowsiness Detection of Driver

Drowsiness Detection of Driver

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： B Jyothi Karthik Seethina Pyla Bhavani Chenna Jayanth Dept. of EEE Vignan's Institute of Information Technology Visakhapatnam Visakhapatnam AP India K L Deemed to be University Vaddeswaram India

During this fast-paced developing world we want advanced procedures to spot the real time methods to identify to save a life, that is valuable in saving a family from negatives if road accidents occur. This paper justifies the hazards on road that happen due to driver being drowsy. Previous studies conclude more hazards being created due respect of drowsiness. This project articulates different types involved in identifying the driver condition and warns the person. The 2 ways we can identify the state of driver at wheel is by using following techniques. First is dependency of psychology whereas other is based on behaviour. In continuous detection tech world, driver exhaustion acknowledgment is one amongst important business. We discuss the driver is alerted based on the response from face. In regard with this machine learning the subtopic in AI i.e., computing is employed in specified way of predicting state of a person to generate data which tend to increase the Ideology of “safety first” on the highways and road. AI could be a system that is having capacity to adapt to new learning by continuously improving without requiring the need to modify or adapt to the new technology and the programs. during this paper we include literature survey of previous studies with respect to person drowsiness detection and attention buying technology. We adapt to learn the Perclos or Euclidian algorithm, cascade classifier based on haar, OpenCV, Python that are crucially employed to detect the driver. At last, we undergo the future study and scope with regarding to advancements on the study with particular project.

关键词： Portable computers Face recognition Roads Wheels data science Cameras Hazards

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machine learning for activity recognition: hip versus wrist data

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PHYSIOLOGICAL MEASUREMENT 2014年第11期35卷 2183-2189页

作者： Trost, Stewart G. Zheng, Yonglei Wong, Weng-Keen Queensland Univ Technol Sch Exercise & Nutr Sci Inst Hlth & Biomed Innovat Kelvin Grove Qld 4059 Australia Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Problem addressed: Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology: 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results: Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%);acceptable classification accuracy for lying down (88.3%) and basketball (81.9%);and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%);acceptable classification accuracy for basketball (86.0%);and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact: Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

关键词： accelerometer objective monitoring children

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Automated Dynamic Stuttered Speech recognition System and Conversion System using Mel Filter And Enhanced Logistic Regression Model

Automated Dynamic Stuttered Speech Recognition System and Co...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： K. Nirosha N. Venkatesh School of CS&AI SR University Warangal Telengana India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Confidence and communication are both negatively impacted by stuttering, a speech condition that affects more than 70 million people globally. To improve fluency and intelligibility, the Stuttered Speech recognition (SSR) system uses cutting-edge technologies such as the Weighted Mel Frequency Cepstral Coefficient algorithm and machine learning (ML) models to detect and fix speech defects. It can transform text into speech for assured communication and includes noise reduction for clarity. This goes beyond the scope of conventional speech recognition and holds great promise for helping stuttering people lead more fulfilling lives and be more integrated into society at large. The characteristic features of stuttering speech are an abundance of initial repetitions and prolongations. As a result, the stuttering episodes can be categorized using sound analysis. Finding specific stuttering occurrences, such as prolongations and repeats, in stuttered speech using a feature extraction technique is described in this study. To determine how effectively it can identify stuttering prolongations and repetitions, we apply the famous Mel Frequency Cepstral Coefficient (MFCC) feature extraction. Stuttering is a disease of neurodevelopment characterized by a lack of fluency in conventional speech patterns. Prolonged or repeated sounds, syllables, sentences, or phrases are examples of disfluency. Stuttering detection is challenging and complex since it involves pathology, psychology, acoustics, and signal processing, all of which make it an intriguing interdisciplinary domain study challenge. A thorough analysis of stuttering and disfluency classification methods is presented based on acoustic data as well as statistical and machine learning and deep learning approaches. This research is helpful for scholars, academicians and medical experts to identify the limitations in traditional models and to design efficient solutions for stuttered speech classification.

关键词： Pathology machine learning algorithms Signal processing algorithms Psychology Speech recognition Speech enhancement Signal processing Feature extraction Mel frequency cepstral coefficient Medical diagnostic imaging

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Chronic Kidney Disease Analysis using Various Biological Parameters under machine learning Techniques

Chronic Kidney Disease Analysis using Various Biological Par...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： M. Suresh E. Nirmala M. Maragatharajan Manish Yadav School of Computing Science and Engineering VIT Bhopal University Kotri Kalan Madhya Pradesh School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kotri Kalan Madhya Pradesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Kidney chronic disease (KCD) has become one of the most critical illnesses which should be diagnosed properly within a certain limit of time for better treatment. Nowadays different algorithms being used for treatment of patients. By using the best predictory model, doctor can easily predict the disease on time. For this verdict, Kidney Chronic Disease Prediction has been discussed in this article. In this article various datasets have been used by the different article authors to predict the disease which are pre-processed with different methods like Univariate Feature Selection (UFS) and Recursive Feature Elimination (RFE), data mining techniques, etc. which are further used to train and test various ML models get the accuracy by predictory models. The scores gained by the predictory models is then validated to check the precision of the output accuracy of by the model using methods like performance matrices, various cross validation techniques, Mathew Correlation Coefficient (MCC). This whole article is all around the analyses of different articles based on the machine learning and validation techniques with respect to the accuracy. Most of the models attained the accuracy of 99 percent and above. In this article, along with machine learning models it also discussed about two neural networks, which attained the accuracy between 93 to 98% with a good precision value.

关键词： Analytical models Renewable energy sources Accuracy Biological system modeling machine learning Predictive models Feature extraction data models Tuning Diseases

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Big data and Deep learning in Smart Cities: A Comprehensive dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

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Computing and machine Intelligence (ICMI), international conference on

作者： Victor Adewopo Nelly Elsayed Zag Elsayed Murat Ozer Constantinos L. Zekios Ahmed Abdelgawad Magdy Bayoumi School of IT University of Cincinnati Cincinnati USA Dept. of Elec. & Comp. Eng. Florida International University Florida United States School of Eng. & Tech. Central Michigan University Michigan USA Dept. of Elec. & Comp. Eng. University of Louisiana Lafayette Louisiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： Computer vision Visualization Smart cities Tracking Video sequences Transportation Transforms

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Dual-Channel Decoupling Marching with Density Relationship Global Fusion for Few-shot Object Detection

Dual-Channel Decoupling Marching with Density Relationship G...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Futian Wang Yu Yi Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China AHU-IAI AI Joint Laboratory Anhui University Hefei China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Traditional deep learning approaches to object recognition typically require large datasets, and acquiring high-quality annotated data prohibitively expensive. Consequently, there has been a growing interest in few-shot object detection, which aims to classify and detect novel classification with only a limited number of samples. Existing methods do not fully exploit feature matching between support images and query images. We propose called the dual-channel decoupled matching with density relationship global fusion method for few-shot object detection. The dual-channel decoupled matching backbone network is specifically designed to mitigate the mutual interference between support features and query features, enabling the extraction of diverse information and improving the model's generalization capability. In order to perceive a more comprehensive feature information, the density relationship global fusion module utilizes feature information of different scales to obtain a more comprehensive feature representation. Extensive experiments conducted on the PASCAL VOC datasets validate the efficacy of our proposed method.

关键词： Deep learning Computer science Object detection Interference Feature extraction Object recognition data mining

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