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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kumar, P. Jagdish Alzubaidi, Laith H. Jasim Nagarjuna, N. Deshmukh, Ram Madhura, G.K. AVN Institute of Engineering and Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq CVR College of Engineering Department of CSIT Telangana India SR University Department of Electrical and Electronics Engineering Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350317558

Amidst the proliferation of Industry 4.0, the integration of artificial intelligence and smart techniques has emerged as a focal point in discussions surrounding industrial cyber-physical systems (CPS). It is still very difficult to detect anomalies in a way that protects security and productivity, especially when there isn't much labeled data available for cyber-physical security protection. A novel method called the Few-Shot Learning model with Siamese Neural Network (FSL-SNN) is presented in this paper with the goal of improving the accuracy of intelligent anomaly detection in industrial CPS and reducing the over-fitting problem. To calculate the distances between input samples using their optimum feature representations, a Siamese encoding network is developed. To bolster the efficiency of the training process, a robust cost function is designed, encompassing three specific losses. The culmination of these efforts results in the development of an intelligent anomaly detection algorithm. The results of experiments conducted on two datasets - one with sparse labels and the other fully labels shows the notable improvements that the proposed FSL-SNN achieves when it comes to lowering false alarm rate (FAR) of 0.041 and raising F1 score of 0.975 for intrusion signal identification in the context of industrial CPS security protection compared to the existing Siamese Convolutional Autoencoder. © 2024 IEEE.

关键词： Anamoly Cyber Physical System Few Shot Learning Intrusion Detection Siamese Neural Network

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Comparative Analysis of Dimensionality Reduction Techniques in Machine Learning Models for Liver Disease Detection

Comparative Analysis of Dimensionality Reduction Techniques ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Manish Rai Hemlata Parmar Shivangi Singh Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Liver disease is a significant global health concern that necessitates accurate and timely diagnosis. This review paper explores the application of machine learning techniques, specifically focusing on the impact of dimensionality reduction methods, in liver disease prediction. This study evaluates both linear and non-linear dimensionality reduction techniques to assess their performance, accuracy, and computational efficiency in the context of liver disease prediction. Through a comprehensive review of existing research and experimental results, this study reveals that while non-linear methods offer advantages in data visualization, linear methods like PCA generally outperform them in terms of model accuracy and computational efficiency. This review provides valuable insights for researchers and healthcare professionals seeking to optimize machine learning models for liver disease diagnosis. By understanding the strengths and limitations of different dimensionality reduction techniques, researchers can select the most appropriate methods to improve the accuracy and efficiency of liver disease prediction models.

关键词： Dimensionality reduction Analytical models Accuracy Liver diseases Reviews Computational modeling Medical services Predictive models Computational efficiency Principal component analysis

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Detecting Objects in Surveillance Videos with Deep Neural Networks for Crime Scene Analysis

Detecting Objects in Surveillance Videos with Deep Neural Ne...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAai 2023

作者： Sudha, I. Kirubasri, G. Deivakani, M. Singh, Vijendra Pratap Kumar, T.Rajesh Niranjanamurthy, M. Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications U.P Varanasi India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Department of Ai and Ml Karnataka Bangalore India

ISBN: (纸本)9798350348910

Surveillance systems are critical components of modern security and forensic investigation, and their efficacy is strongly reliant on precise object detection in video recordings. This study looks into the use of deep neural networks to improve object detection in surveillance films for crime scene analysis. This study investigates the capabilities of cutting-edge deep learning architectures in recognizing and classifying objects in various surveillance contexts. The study's findings are extensive, including detection accuracy metrics for numerous item classes such as 'Person,' 'Vehicle,' and 'Suspicious Item.' Precision values range from 0.78 to 0.92, recall values range from 0.82 to 0.90, and F1 scores range from 0.80 to 0.90, demonstrating the models' ability to recognize objects accurately, but with variances among item categories. This study also looks into computational performance, offering information about inference times and GPU utilization. Inference times for ResNet-50 and YOLOv3 are 15 ms and 20 ms, respectively, with GPU use percentages of 75% and 90%. These findings provide useful information for picking models that fulfill real-time processing needs while optimizing computational resources. The research also examines the connection between video resolution, detection speed (up to 30 frames per second), and average detection accuracy. Lower resolutions allow for faster processing, but at the expense of accuracy, whilst higher resolutions provide finer details at the expense of larger computational needs. These trade-offs are critical considerations when building surveillance systems to meet certain operational requirements. © 2023 IEEE.

关键词： Object detection

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Lasso Regression Model of HSV Color Space Masking Vision Based Whole Coffee Bean Processing Agtron Measurement

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Journal of Institute of Control, Robotics and Systems 2024年第11期30卷 1245-1253页

作者： Lee, Ye Chan Noh, Yi Hyun Noh, Yun Seo Kim, Ju Song Ryu, Kwangyeol Kim, Byeong Hak Department of Industrial Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Pusan National University Korea Republic of AI Research Center for Manufacturing Systems Korea Institute of Industrial Technology Korea Republic of

The rapid increase in coffee consumption has led to a significant expansion in production scale and variety within the agricultural regions of the global coffee belt. Recent coffee harvested in varies specious and processed in various ways, and the functionality of processing equipment is advanced to optimize its quality. In particular, coffee roasting equipment has evolved to incorporate complex structures and advanced electronic, mechanical, and heat/air control technologies for processing harvested green coffee beans. One of the most critical quality factors in the roasting process is the degree of roast, which is measured by the bean color (Agtron) value. The conventional method for Agtron measurement involves grinding the beans and using vision sensors. However, the conventional method shows difficult and time-consuming. The method for measuring the color of whole beans without grinding is simpler, but it has limitations in accuracy due to variations in shading and background images. This study proposes a method to accurately measure the Agtron color of whole coffee beans without grinding. The proposed method employs a preprocessing technique that separates the measurement area from the background area through color histogram analysis. To enhance measurement accuracy, we suggest using a Lasso regression model, which mitigates overfitting problems. Experimental results demonstrate that the proposed method achieves an improved accuracy compared with traditional regression methods and neural network-based approaches. © ICROS 2024.

关键词： Grinding (machining)

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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Framework for Evaluating the Carbon Footprint of Academic Machine Learning Endeavor

Framework for Evaluating the Carbon Footprint of Academic Ma...

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Communications and computer science (InCCCS), International Conference on

作者： Karthik K Pai Monika Goyal Department of Computer Science And Engineering (AI&ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

Carbon foot printing has gained massive importance in our tryst to move towards sustainability. With machine learning seen as a solution for many real world problems, sustainability in machine learning is important. Hence the motivation to develop this framework which can aid in foot printing current activities irrespective of architecture and platform. Through various iterations of preliminary analysis and through research, framework has been implemented and successfully tested on multiple architecture models to accurately compute the same.

关键词： Training Printing Analytical models Publishing Computational modeling Machine learning computer architecture

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A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation 4th

A Novel Architecture for a Medical Image Recognition System ...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Parashar, Jyoti Upreti, Kamal Fatima, Iram Gangwar, Divya Saini, Ashok Kumar Vats, Prashant Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Computer Science Christ University Delhi NCR Campus Ghaziabad India Department of AI & ML Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Management Babu Banarasi Das Institute of Technology & Management New Delhi India Department of Computer Science & Engineering School of Computer Science & Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819751990

Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ml-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK AND IMAGE PROCESSING 15

FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK A...

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

作者： Srilatha, Boddu Ithvika, C.H. Meenakshi, Patil Pradhan, Sasmita Kumari Rani, K. Pushpa Jayanth, M. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad500043 India Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad500043 India Department of of CSE-CyS DS and AI&DS VNR VJIET Bachupally Hyderabad India

ISBN: (纸本)9798350370249

The increased use of modern printing and scanning technologies has led to a significant rise in counterfeit currency production, posing a serious threat to global economies. To tackle this growing issue, our project, titled "Fake currency detection using Convolutional Neural Networks and Image Processing," introduces an innovative solution that utilizes artificial intelligence (ai) and machine learning for efficient counterfeit detection. Financial institutions, banks, and businesses are facing heightened vulnerability to counterfeit currency, resulting in considerable financial losses and a decrease in the value of genuine money. Current currency detection systems often rely on time-consuming traditional methods and manual inspection, which are prone to human error. Even the counterfeit detection machines in use have limitations when it comes to identifying sophisticated counterfeit notes. Our project addresses these challenges by proposing an advanced system that integrates convolutional neural networks (CNNs) and image processing techniques. Given the advancements in printing and scanning technologies, counterfeiting has evolved into a more sophisticated and widespread problem. Traditional currency detection methods, rooted in hardware and image processing, have proven to be inefficient and time-consuming. Hence, there is a critical need for a more robust and rapid solution to detect counterfeit currency. Our proposed approach employs a transfer-learned CNN, a deep learning model trained on a dataset comprising real and fake currency images. The CNN learns the intricate features of both genuine and counterfeit banknotes, allowing it to accurately identify fake currency in real-time. The transfer learning process enables the CNN to leverage knowledge gained from a diverse dataset, improving its ability to recognize subtle patterns associated with counterfeit notes. The primary components of our project include a diverse dataset with images of real and fake currenc

关键词： Convolutional neural networks

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An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

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computer Systems science & engineering 2023年第2期44卷 1309-1324页

作者： Junaid Rashid Sumera Kanwal Muhammad Wasif Nisar Jungeun Kim Amir Hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

In project management,effective cost estimation is one of the most cru-cial activities to efﬁciently manage resources by predicting the required cost to fulﬁll a given ***,ﬁnding the best estimation results in software devel-opment is ***,accurate estimation of software development efforts is always a concern for many *** this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artiﬁcial neural network(ANN).An artiﬁcial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model *** state-of-the-art publicly available datasets are used for *** backpropagation feed-forward procedure used a training set by iteratively processing and training a neural *** proposed model is tested on the test *** estimated effort is compared with the actual effort *** results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

关键词： Software cost estimation neural network backpropagation forward neural networks software effort estimation artiﬁcial neural network

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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

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SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ml) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ml approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ml is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPImlM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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