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An Intelligent IoT-Driven Smart Shopping Cart with Reinforcement learning for Optimized Store Navigation 1

An Intelligent IoT-Driven Smart Shopping Cart with Reinforce...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Vadivel, P. Sundara Karthika, B. Robinson, Y. Harold Krishnan, R. Santhana Rachel, L. Sundararajan, S. Saveetha Engineering College Department of Artificial Intelligence & Machine Learning Chennai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Francis Xavier Engineering College Department of Computer Science and Engineering Tirunelveli India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Latha Mathavan Engineering College Department of Electronics and Communication Engineering Madurai India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India

ISBN: (纸本)9798350359756

In the retail commercial sector, various advancements in IoT and AI have industrially renovated the domain. Due to the introduction of boosted connectivity and availability of intelligent systems pave way for redefined shopping experience. The current research work highlights a pioneering tactic to improve shopping experiences. It integrates an intelligent IoT-driven Perceptive shopping cart and reinforcement learning techniques for optimized store navigation. In the research work, the salient features of the two existing deep learning algorithms viz. Reinforcement learning and Q Network with Long Short-Term Memory are integrated and an innovative algorithm called Reinforced Q-Network with Long Short-Term Memory (ReQL-Net) is proposed. The ReQL-Net algorithm authorizes the perceptive cart to unconventionally acquire effectual navigation strategies. Extensive simulation results show that The ReQL-Net algorithm outperforms other reinforcement learning or Q-Network methods in terms of Navigation Efficiency, Exploitation Trade-off and Convergence Rate. Using the proposed algorithm, the customer can personalize his shopping experience. Also the shopkeeper can designed for individual customer experiences. This information can be used to create targeted marketing strategies. Also, such researches on smart shopping cart opens up new possibilities for the future of intelligent shopping systems which can adapt to changing retail environments. © 2023 IEEE.

关键词： Edge computing

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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Exploring Advanced Techniques in Computer and Network Forensics for Enhanced Cybersecurity

Exploring Advanced Techniques in Computer and Network Forens...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nagpal, Amandeep Kumar, S Vinod Reddy, Uma Radha, K. Kumar, Ashwani Abdulla, Namaat R. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India St. Martin's Engineering College Dept. of It Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Because cybersecurity is continually changing, it's crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The machine learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of a Predictive Model for Kidney Stone Formation Using Deep learning Techniques

Development of a Predictive Model for Kidney Stone Formation...

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2024 International Conference on artificial intelligence and Emerging technology, Global AI Summit 2024

作者： Mohammad, Faz Sharma, Gajendra Khandelwal, Kritika Arif, Abdul Vani, M. Sree Sathyanarayana, Cheruku G. L. Bajaj Institute of Technology and Management Department of Artificial intelligence and Machine learning Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Manipal University Department of Business Administration Jaipur India Aditya College of Engineering and Technology Mechanical Department Surampalem India Bvrit Hyderabad College Of Engineering For Women Department of Cse Hyderabad India Vignana Bharathi Institute of Technology Jntuh Univeristy Hyderabad India

ISBN: (纸本)9798350379716

This research investigates the development of a predictive model for kidney stone formation using deep learning techniques, specifically Convolutional Neural Networks (CNN), VGG 19, and Deep Belief Networks. Utilizing a dataset of 920 CT scans of images with varying sizes and locations, the model is used to predict the presence and location of kidney stones within the scan and the size of the kidney stones when present. The images undergo preprocessing, which includes standardizing the resolution, reducing noise, increasing contrast, and ensuring the spatial registry of all the images. Through this preprocessing, we are able to extract features from the images which, when fed through the models, can detect the subtle patterns that indicate the presence of the kidney stones. The dataset is split into two portions: one which the model is trained upon which contained 70% of the dataset and the other for validation, which encompassed 80% of the dataset. Various performance metrics were used to evaluate the performance of the models including accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic (ROC) Curve AUC-ROC. The results have shown that the CNN model consistently led to better results than either the VGG 19 or DBN model across either the testing or validation data partitions;it yielded the highest levels in accuracy, precision, recall, and F1 score. The model which trained on 80% of the dataset performance yielded superior results compared to the model which trained on the 70% dataset, which further demonstrates the importance of size in training. The technique used in this research has the potential to revolutionize kidney stone detection and diagnoses, ensuring better planning for clinical decisions in patients with kidney stones. © 2024 IEEE.

关键词： Convolutional neural networks

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Diagnosis of Advanced Diabetic Retinopathy from Fundus Image Using machine learning

Diagnosis of Advanced Diabetic Retinopathy from Fundus Image...

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artificial intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Bhagyashri Mankar Bhushan Bawankar Diksha Gabhane Mohini Wanjari Department of Information Technology Yeshwantrao Chavan College of Engineering (YCCE) Nagpur India Department of Artificial Intelligence and Machine Learning J.D. College of Engineering Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Diabetic Retinopathy is an eye disease which mainly caused to the diabetic patients. The patients who have been suffering from diabetes since long time have major chances to suffer from Diabetic Retinopathy (DR) as well. If the DR is not detected early, the patient may experience vision loss, so early detection of this disease is very important. Micro-aneurysm and exudates are the features from which we can identify whether patients suffer from DR or not. Identifying retinal micro-aneurysms and exudates from the retinal fundus image is the primary goal of the proposed work to test for diabetic retinopathy. Firstly, the pre-processing is performed on Fundus image. Preprocessing techniques include adaptive median filtering, adaptive histogram equalization, and grey scale conversion. Feature extraction is utilized to extract the features from the fundus image after pre-processing. The extracted features are act as an input to the SVM classifier. The fundus image is classified as normal or impacted using a vector machine.

关键词： Support vector machines Diabetic retinopathy Visualization Histograms Quantum computing Filtering machine learning Feature extraction Retina Vectors

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Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques 6

Smart Telemedicine Using IoT by Integrating 5G and Block-Cha...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Choudhary, Shailee Lohmor Dixit, Rinku Sharma Das, Deepak Ranjith Singh, K. Dinesh Babu, V. New Delhi Institute of Management Department of Artificial Intelligence & Machine Learning - Data Science New Delhi India Karunya Institute of Science and Technology Agricultural Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350304480

There are many medical applications for the Internet of Health Things (IoHT). In order to offer patients with more intelligent and effective health diagnoses, contemporary IoHT incorporates wellness items such sensors with continuously monitored medical equipment for the evaluation and handling of a medical chart. Using the combination of 5G networks and ethereum technology, we presented the IoT using a fog medical decision assistance system in this study for the forecast and monitoring of illness along with its severities. Due to its visibility, a frame provides a method for keeping and exchanging knowledge which is safe. Ethereum has various uses in the medical industry and therefore can enhance monitoring equipment, clinical study storage systems, mobile medical software, exchanging and keeping of digital media files, and insurer details stockpiling. The suggested structure will assemble a cloud provider will hold client information from the health equipment that's going to be connected to the individual, along with any pertinent health records. The use of 5G with cryptocurrency technologies allows the reliable transfer of patient information at high transmission rates with short reaction times. In addition, a Deep Neural (NN) classification is employed to forecast illnesses as well as the degree of underlying severity. The suggested model is tested using a variety of classifiers. In order to determine which classification is appropriate for the information, the effectiveness of various classifier is compared by contrasting the results. The NN improved a 98.98 average accuracy. This NN is additionally trained on the information such so this can forecast the outcome of the information category that carries no labels. A taught Naive Bayes is more accurate than conventional classifications at making predictions and effectively displaying the findings. © 2023 IEEE.

关键词： 5G Blockchain CDSS DNN IoHT IoT

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Grammar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Geetha, M. Vijaya, K. Subramanian, K.M. Rajesh Kanna, P. Rajeshkumar, G. Karthika, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Kongu Engineering College Department of Computer Science and Design Erode India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institue of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India

ISBN: (纸本)9798350313987

In the digital era, mos t of the communication is happening through online platforms. English plays a vital role for better understanding. Good grammar helps in framing a better sentence. This demands for the development of an automatic Grammar Error Correction (GEC) tool. Many research works have been initiated to make Grammar Error Correction possible by using various machine learning algorithms. Some of the algorithms are Naive Bayes, BERT, Support Vector machine, and ULMFIT. Among these algorithms BERT gives a comparatively best accuracy rate. The fundamental goal of this research work is to compare these algorithms with respect to accuracy Here, NLP is used for analyzing the grammatical corrections in the sentence. Further, this study can be extended to overcome the drawbacks faced by the BERT algorithm such as increase in time cons umption for training and cos t. © 2023 IEEE.

关键词： Bidirectional Encipher Representations from Instruments Grammar Error Correction Natural Language Processing Universal Language Model Fine-Tuning

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machine learning-Driven Blockchain for Enhanced Transparency in Government Public Records 13

Machine Learning-Driven Blockchain for Enhanced Transparency...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Lakhanpal, Sorabh Reddy, Ch Vijendar Reddy, Uma Varkale, Akshay Kumar, Ashwani Albawi, Ali Lovely Professional University Phagwara Pakistan Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M.P. Bhopal India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201306 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350305463

With more things done online, citizens have never wanted government data to be more available and dependable. This paper proposes a solution using blockchain and powerful algorithms. The recommended approach uses blockchain's immutability and safety, machine learning to discover outliers, and NLP to organize data. This coordinated strategy automates and opens government records to increase data quality, accessibility, and speed. Several tests and performance assessments compared the recommended solution to the traditional record-keeping method. Finding anomalies is faster using machine learning anomaly detection and natural language processing record sorting. This ensures data accuracy and clarity in real time. Spreading blockchain keeps documents secure and unchangeable. This makes changing difficult for those who shouldn't. The study found that the proposed strategy might improve public record management. It improves transparency, a crucial trait for responsible leadership, in a powerful, safe, and automated way using blockchain technology and cutting-edge algorithms. © 2024 IEEE.

关键词： Transparency

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Integrating machine learning with Binary Integer Programming for Optimal PMU Placement in Dynamic Power Networks 7

Integrating Machine Learning with Binary Integer Programming...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Uma Reddy, N.V. Anuradha, R. Lakhanpal, Sorabh Read, Rawya Subhashini, P. Divetia, Kanan New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Institute of Aeronautical Engineering Telangana Hyderabad50043 India Lovely Professional University Punjab Phagwara India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

PMUs or synchronized PMUs are rapidly becoming essential in today's power systems acting as monitors in the advancement of mainstream grid supervisory, protective, and controlling systems. This paper presents an improvement of the existing methodology for PMU placement by incorporating machine learning algorithm coupled with binary integer programming in order to feed real-time grid conditions and demand forecasts into the algorithm. Addressing four primary constraints: 1) observability in fully and partially observable cases;2) conventional measurements across various cases;3) network robustness against single PMU or a communication line;and finally, 4) flexibility with regards to increasing or decreasing failure rates, this augmented approach includes, but does not limit to, machine learning-based predicative analysis for a dynamic power grid management. Employed on IEEE 14-bus, a 21-bus 400-kV real power system, as well as the IEEE 30-bus system, strategy helps in creating a cost-effective, secure and smart monitoring system. © 2024 IEEE.

关键词： Observability

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A Survey on Fast Task Adaptation Techniques in machine learning

A Survey on Fast Task Adaptation Techniques in Machine Learn...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Ramesh G Kiran Raj K M Shantveer Kesti Charandeep B S Manvith Suvarna Akash Nayak Department of ISE NMAM Institute of Technology Nitte(Deemed to be University Udupi Karnataka India Department of Artificial Intelligence and Machine Learning Alva's Institute of Engineering and Technology Mangalore Karnataka India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Meta-reinforcement learning is a cutting-edge approach that facilitates swift and adaptable responses to new tasks in dynamic environments with significant resource limitations. Utilizing meta-learning principles offers substantial benefits, enabling Meta-RL frameworks to effectively learn from minimal data samples while improving their adaptability and resilience in various domains, including IoT, robotics, autonomous vehicles, and energy management. This paper examines different Meta-Rreinforcement learning approaches and frameworks, such as actor-critic models, Bayesian techniques, attention-driven networks, and adversarial learning methods. The adoption of these frameworks highlights their capabilities.

关键词： Metalearning Training Surveys Adaptation models Scalability Robustness Bayes methods Internet of Things Vehicle dynamics Robots

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