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Development of an AI based Priority Formation Mechanism in Criminal Law

Development of an AI based Priority Formation Mechanism in C...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Kashish Sharma Ankita Amburle Vijay Shelake Ashvini Ashok Chauhan Dept. of Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Fr. Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Nowadays, there is a necessity to tackle a number of pending crime cases across different parts of the world through effective scheduling and management. AI and ML can be used in handling unfinalized criminal cases in India can be one of the possible ways of improving the functioning of the criminal justice system in this country. The approach to this project is to create an AI system that will filter through the open cases and then rank them according to complexity, time-sensitivity, prior performances, and location. Since priority in a case is explained through the use of a supervised learning based on selected set, then law enforcement as well as legal personnel are able to shift most of their attention to other significant cases. Despite feature engineering and data preprocessing being in the construction of the system from the data collected from the various sources such as court records. This means that there is than models such as; Random Forest, Apriori and Rule-Based in an effort to establish the most suitable model. Perhaps the application of this AI based mechanism to form priorities could aid India in better organization of resources and with the assistance of backlog decrease – the justice system in India. But as the right touch to measure artificial use is considered, themes of privacy, bias, and fairness must also be addressed appropriately.

关键词： Renewable energy sources Privacy Machine learning algorithms Supervised learning Organizations Scheduling Personnel Artificial intelligence Random forests Sorting

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Revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely Detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Durai, S. Jaganathan, D. Prabhu, V. Harini, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Differentially Private Stochastic Convex Optimization for Network Routing Applications

Differentially Private Stochastic Convex Optimization for Ne...

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IEEE Conference on Decision and Control

作者： Matthew Tsao Karthik Gopalakrishnan Kaidi Yang Marco Pavone Lyft Data Science Dept. of Aeronatutics and Astronautics at Stanford University Dept. of Civil Engineering the National University of Singapore

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destination (OD) of all requests in the network. However, privacy considerations make it challenging to share individual OD data that would be required for computing optimal policies. Privacy can be particularly challenging in standard network routing problems because sources and sinks can be easily identified from flow conservation constraints, making feasibility and privacy mutually exclusive. In this paper, we present a differentially private algorithm for network routing problems. The main ingredient is a reformulation of network routing which moves all user data-dependent parameters out of the constraint set and into the objective function. We then present an algorithm for solving this formulation based on a differentially private variant of stochastic gradient descent. In this algorithm, differential privacy is achieved by injecting noise, and one may wonder if this noise injection compromises solution quality. We prove that our algorithm is both differentially private and asymptotically optimal as the size of the training set goes to infinity. We corroborate the theoretical results with numerical experiments on a road traffic network which show that our algorithm provides differentially private and near-optimal solutions in practice.

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Enhancing Dental Diagnosis through Automated Detection of Teeth Diseases in Panoramic Radiographs

Enhancing Dental Diagnosis through Automated Detection of Te...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Majdi Rawashdeh Tariq Bdair DhaiEddine Salhi Software Engineering Dept. Al-Yamamah University Riyadh Saudi Arabia Business Information Technology Dept. PSUT Amman Jordan Data Science Dept. PSUT Amman Jordan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Edge learning has shown promising solutions to address diseases by exploiting edge computing capabilities. On the other hand, the analysis of dental radiographs is a crucial step in clinical routine practice since the dentist must evaluate tooth-related issues throughout the clinical diagnosis. Further, errors in manual human analysis may lead to false predictions. Fortunately, the automated approach for identifying and categorizing dental issues can help in the early diagnosis of diseases and prevent tooth loss. This paper aims to create a model that may offer a second viewpoint and simplify dental diagnosis for dentists. The proposed method utilizes state-of-the-art deep learning architecture for object detection. It provides panoramic teeth radiograph images to the proposed network to detect four teeth diseases: conservative (fillings and crowns), implant, RCT (root canal treatment), and bridges. After data preprocessing and augmentation, our framework is used to detect the above teeth diseases with high detection quality and accuracy with mAP and F1-score at 0.902 and 0.86, respectively.

关键词： Irrigation Image edge detection Teeth Object detection Manuals Implants Filling Dentistry Diagnostic radiography Diseases

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Solar Cells, Lambert W and the LogWright Functions

Solar Cells, Lambert W and the LogWright Functions

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Lankireddy, Prabhat Jeevanandam, Sibibalan Choudhary, Aditya Deshmukh, P.C. Roberts, Ken Valluri, S.R. Indian Institute of Technology Tirupati Dept. of Electrical Engineering Tirupati India Indian Institute of Technology Bombay India Center for Machine Intelligence and Data Science Mumbai India Indian Institute of Technology Tirupati Dept. of Mechanical Engineering Tirupati India Purdue University Dept. of Mechanical Engineering West LafayetteIN United States Indian Institute of Technology Tirupati Center for Atomic Molecular and Optical Sciences & Technologies Dept. of Physics Tirupati India Dayananda Sagar University Dept. of Physics Bengaluru India University of Western Ontario Dept. of Physics and Astronomy LondonON Canada King's College University of Western Ontario Dept. of Physics and Astronomy Dept. of Management Economics and Mathematics LondonON Canada

ISBN: (纸本)9798350327816

Algorithms that calculate the current-voltage (I-V) characteristics of a solar cell play an important role in processes that aim to improve the efficiency of a solar cell. I-V characteristics can be obtained from different models used to represent the solar cell, and the single diode model is a simple yet accurate model for common field implementations. However, the I-V characteristics are obtained by solving implicit equations, which involve repeated iterations and inherent errors associated with numerical methods used. Some methods use the Lambert W function to get an exact explicit formula, but often causes numerical overflow problems. The present work discusses an algorithm to calculate I-V characteristics using the LogWright function, a transformation of the Lambert W function, which addresses the problem of arithmetic overflow that occurs in the Lambert W implementation. An implementation of this algorithm is presented and compared against other algorithms in the literature. It is observed that in addition to addressing the numerical overflow problem, the algorithm based on the LogWright function offers speed benefits while retaining high precision. © 2023 IEEE.

关键词： I-V characteristics Lambert W function LogWright function Single Diode Model Solar Cell

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FCS-MPC with computational Efficiency and dv/dt Mitigation in Multilevel Converters

FCS-MPC with Computational Efficiency and dv/dt Mitigation i...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Castillo, Cristian García, Cristian Sanchez, Daniel Araya, Baldomero Ma, Chenwei Davari, S. Alireza Long, Bo Rodriguez, Jose Universidad de Talca Dept. of Electrical Engineering Curico Chile Southwest Jiaotong University School of Electrical Engineering Chengdu China Universidad Andres Bello School of Electrical and Data Engineering Santiago Chile University of Electronic Science and Technology of China Institute of Electronic and Information Engineering Guangdong China Universidad San Sebastian Center for Energy Transition Santiago Chile

ISBN: (纸本)9798350376067

Finite Control Set Model Predictive Control (FCS-MPC) provides accurate voltage and current control in multilevel converters, but computational complexity remains, especially in medium voltage (MV) and high power (HP) applications. This paper proposes an enhanced control strategy for a 5-level Active NPC converter, improving the approach presented in [1] to reduce computational burden and mitigate undesired voltage level jumps and high dv/dt. Simulation results demonstrate improved performance compared to standard FCS-MPC and the original [1] strategy, showcasing reduced voltage level jumps and enhanced current tracking in steady-state and transient scenarios. The proposed enhancement shows promise in improving the efficiency and reliability of multilevel converters in MV and HP applications, with implications for further research in control optimization and performance enhancement. © 2024 IEEE.

关键词： Model predictive control

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AI-Based Home Automation Using Voice Recognition and Biometric Finger Print Authentication

AI-Based Home Automation Using Voice Recognition and Biometr...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Bhasha Pydala M Bhargavi Balaji Vyshnavi Machavaram Gopi Krishna Aravadasari Lenin Kumar K Hima Bindu dept. of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India dept. of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

The proposed home automation system introduces a cutting-edge solution that harnesses the power of Artificial Intelligence (AI) to upgrade residential spaces into smart, dynamic, and user-friendly environments. With a simple vocal command, the AI system swiftly and precisely executes tasks. The existing home automation system lacks in authentication so to ensure security and personalization, the proposed system utilizes fingerprint module integrated with a microcontroller like Arduino Uno and Bluetooth module. It interfaces seamlessly with a fingerprint sensor, providing reliable user authentication. This ensures that the smart home system responds only to authorized users, enhancing overall safety. The implementation includes essential components such as Arduino Uno, Bluetooth modules, regulated power supply and fingerprint module. This innovative system showcases the potential of integrating AI into homes, making them not only intelligent but also adaptable to the different requirements of each resident. With speech recognition and fingerprint authentication, this system offers a glimpse into a future where homes are not just structures but responsive companions that enhance the quality of life.

关键词： Bluetooth Authentication Speech recognition Smart homes Fingerprint recognition Safety Security

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Seva, Banothu Jain, Ratnesh Khare, Neha Choudhury, Hussain Ahmed Chakravarthi, M Ayyappa Tomer, Nitin MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Design M.P Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P Bhopal462044 India Barak Valley Engineering College Department of CSE Assam Karimganj India KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India GGITS Cyber Security & Blockchain Dept of IOT M.P. Jabalpur India

ISBN: (纸本)9798331539948

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. Security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Augmentation for Imbalanced Deep Learning 39

Efficient Augmentation for Imbalanced Deep Learning

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Dablain, Damien A. Bellinger, Colin Krawczyk, Bartosz Chawla, Nitesh V. University of Notre Dame Lucy Family Institute for Data & Society Dept. Computer Science and Engineering Notre DameIN46556 United States National Research Council of Canada OttawaK1A 0R6 Canada Virginia Commonwealth University Dept. Computer Science RichmondVA23824 United States

ISBN: (纸本)9798350322279

Deep learning models may not effectively generalize across under-represented or minority classes. We empirically study a convolutional neural network's (CNN) internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes. This insight enables us to design an efficient three-phase CNN training framework for imbalanced data. The framework involves training the network end-to-end on imbalanced data to learn feature embeddings, performing data augmentation in the learned embedding space to balance the training data distribution, and fine-tuning the classifier head on the embedded balanced training data. We develop Expansive Over-Sampling (EOS) as a data augmentation technique to utilize in the training framework. EOS forms synthetic training instances as convex combinations between the minority class samples and their nearest adversaries in the embedding space to reduce the generalization gap. The proposed framework improves the accuracy over leading cost-sensitive and resampling methods commonly used in imbalanced learning. Moreover, it is more computationally efficient than standard data pre-processing methods, such as SMOTE and GAN-based over-sampling, as it requires fewer parameters and less training time. The source code for the proposed framework is available at: https://***/dd1github/EOS. © 2023 IEEE.

关键词： Embeddings

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Sustainable Smart Agriculture Advisor through Artificial Intelligence Approach

Sustainable Smart Agriculture Advisor through Artificial Int...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Hariharan, Shanmugasundaram Srivani, Pogula Gogula, Sreenivasulu Venkateswaran, N. Kukreja, Vinay Jose, P. Vardhaman College of Engineering Dept. of Artificial Intelligence and Data Science Hyderabad India Cmr Technical Campus Department of H&s Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Jyothishmathi Institute of Technology & Science Nustulapur Department of Computer Science and Engineering Karimnagar India Centre for Research Impact & Outcome Chitkara University Rajpura India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350375442

Agriculture, constituting the backbone of India's economy and employing 70% of its population, faces critical challenges such as water scarcity, soil degradation, and unpredictable crop yields. Traditional farming methods lack efficiency, contributing to environmental impact and resource inefficiency. To address these issues on smart crop cultivation with artificial intelligence enabled crop advisor sustainable agriculture generation integrates machine learning and data analytic to offer real-time insights and recommendations. This advanced agricultural approach aims to optimize resource utilization, minimize environmental impact, and adapt to changing climatic conditions. By empowering farmers with actionable insights, Smart Crop seeks to revolutionize agriculture, promoting sustainable practices for increased resource efficiency and enhanced resilience against challenges like limited precision farming, climate variability, and yield maximization. This innovative solution endeavors to bridge the gap between traditional farming methods and modern challenges, fostering a more sustainable and productive future for Indian agriculture. The methods analyzed leads to promising results as compared with well known metrics. © 2024 IEEE.

关键词： Smart agriculture

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