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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Sivaparthipan, C.B. Alabdeli, Haider Harshitha, P. Nagendar, Y. Kulkarni, Girija G. Tagore Institute Of Engineering And Technology Department Of Cse Salem India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Menashaki Institute Of Technology Department Of Ai Ml Bengaluru India Sr University School Of Computer Science And Engineering Telangana Warangal India SacramentoCA United States

ISBN: (纸本)9798350381665

The E-commerce market is the most hype and growth sector in the economic, and many countries showing interest on e-commerce to develop the economy. In e-commerce the logistic is the key factor to consider for the profitable growth so focus on the reverse logistic for efficient distribution and increase profit. The reverse logistic is highlighted in this study for decrease the distribution cost and increases the distribution efficiency for growth of the e-commerce market. The study proposes the Hierarchical Density-Based Spatial Clustering of Application with noise (HDBSCAN) clustering algorithm for analyzing the customer behavior to solve route optimization problem. Then mathematical programming model was built for reducing the damages cost it includes fixed cost, green cost, cargo cost. Thereafter, the genetic algorithm was used to solve problem of the model and achieved 93.50% accuracy. The proposed method results show the promising results in solving the route optimization problem with consideration of the customer priority. Moreover, the propose method is greatly used for enterprises in reverse logistics to minimize the cost and time. © 2024 IEEE.

关键词： Electronic commerce

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Competition on robust deep learning

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National science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (ai) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,ai is playing an important ***,although ai is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Prediction of River Water Quality Index and Uncertainty Analysis using Machine Learning Techniques 1

Prediction of River Water Quality Index and Uncertainty Anal...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Alabdeli, Haider Pareek, Piyush Kumar Shivaraj, Shruti Rupavath, Rana Veer Samara Sihman Brahmatej Das, Abhijeet The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Department of Civil Engineering Raichur India Indiana Wesleyan University Department of Computer Science MarionIN United States Department of Civil Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350352931

The increasing demand for clean water in river systems drives the development of new models for water quality prediction. The problem statement is the need for accurate and reliable prediction of water quality indices in various river basins using machine learning techniques. Through the integration of diverse methodologies in the proposed approach aims to overcome limitations associated with conventional methods and emerging in water quality concerns. The dataset used in Water Quality Prediction (WQP) in the river dataset was used to predict the water quality index. The proposed method in this paper consists of preprocessing, data standardization, and data splitting with the parameter and calculating the WQI. The combined machine learning algorithms include Random Forest (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN). The numerical validation of this paper is an MSE of 0.03, an RMSE of 0.09, an MAE of 0.02, and R 2~ 0.12. The existing methods like Water Quality Management Limitations (WQM-WQI), Sequential Minimal Optimization-Support Vector Machine (SMO-SVM), Principal Quality Analysis (PQA-WQI), and Artificial Neural Network (ANN-WQI) are overcome by the proposed method. © 2024 IEEE.

关键词： Adversarial machine learning

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The Effect of Programming Language in Software Bug Prediction 3

The Effect of Programming Language in Software Bug Predictio...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Jayadev, Gopika Gayathri, Kaparthi Babu, Tina Gandiboina, Harika Sudha, Kavya Venkteswalru, Bondu School of Engineering Dayananda Sagar University Department of AI and ML Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474979

Software maintenance is a very important phase in the life cycle of software development. As part of maintenance, we need to identify bugs within the code and fix them for every release. Software bug prediction (SBP) allows us to identify modules in the software that may have the tendency to be buggy. This enables us to perform targeted testing and properly plan maintenance cycles. In most research performed, we observed that dataset of software programs written in C language were used. Each programming language are inherently different and has it's own constructs, practices and nuances. In this research we focus on identifying if there exists a specific classifier that works well for each programming language which in turn is used to analyze the effect of programming language on Software Bug Prediction. Datasets of software written in C, C++ and Java has been collected and the most accurate classifier for each dataset of a programming language has been identified. The ml models used in this paper include Naive Bayes, Decision Tree and Random Forest. © 2022 IEEE.

关键词： Decision Tree Machine Learning Naïve Bayes Random Forest Software Bug Prediction

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Next-Gen Human-computer Interaction: A Hybrid LSTM-CNN Model for Superior Adaptive User Experience

Next-Gen Human-Computer Interaction: A Hybrid LSTM-CNN Model...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Ravindra Changala J. Manoranjini M. Misba Shital Kakad S Sai Deepthi Ponni Valavan M Department of Computer Science and Engineering Guru Nanak Institutions Hyderabad India Department Of AI&ML Rajalakshmi Engineering College Chennai India Department CSE (AI&ML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of AI & DS Vishwakarma Institute of Technology Pune Maharashtra Department IT B V Raju Institute of Technology (Autonomous) Narsapur Hyderabad India Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In the rapidly evolving landscape of human-computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. This hybrid LSTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms. Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. By integrating LSTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LSTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text. One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms. In experimental evaluations, Hybrid LSTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare. In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactiv

关键词： Human computer interaction Adaptation models Scalability Streaming media Feature extraction User experience Robustness Convolutional neural networks Long short term memory Videos

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Vision Beyond Sight: An ai-Assisted Navigation System in Indoor Environments for the Visually Impaired

Vision Beyond Sight: An AI-Assisted Navigation System in Ind...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： A. Ajina R Lochan Mohini Saha R B K Showghi S Harini Department of AI&ML M S Ramaiah Institute of Technology Bangalore India Information Science Engineering RV Institute of Technology and Management Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

This study introduces an innovative visual assistance technology that incorporates a cutting-edge live object recognition system to address the prevalent challenges faced by visually impaired individuals worldwide. In daily life, the visually impaired rely heavily on touch and auditory cues to navigate their surroundings, which underscores the urgent need for technological support. While existing solutions such as Screen Reading software and Braille devices fail to recognize crucial elements, rendering them ineffective in critical situations, our approach aims to revolutionize the lives of the blind and visually challenged on a global scale by providing a second set of eyes without external assistance. With a commitment to creating an inclusive environment, our research goes beyond current solutions by emphasizing assistive technology, which truly makes a difference. The proposed system leverages SSD MobileNet-v2 FPNlite-based object detection combined with an Android mobile application to empower visually impaired individuals through real-time voice-based guidance. By specifically addressing the challenges related to recognition and navigation, this project aims to significantly improve the independence and overall quality of life of those with visual disabilities, thereby contributing to the creation of a more welcoming and inclusive world.

关键词： computer science Visualization Navigation Object detection Rendering (computer graphics) Real-time systems Software

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Leveraging Machine Learning for Pattern-Based Detection of Respiratory Illnesses

Leveraging Machine Learning for Pattern-Based Detection of R...

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Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Anusha Prashanth Shetty Deepthi L Raksha Puthran Surendra Shetty Pavan Hegde Shravan Kumar Department of Master of Computer Applications NMAM Insitute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering (AI&ML) Sahyadri College of Engineering and Management Mangalore India Department of Paediatrics Father Muller Medical College and Hospital Mangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Lung sound analysis presents a promising, noninvasive, and potentially cost-effective approach for detecting respiratory diseases. This research aims to assess the feasibility of using lung sounds for pattern recognition and detection of various respiratory disorders, specifically by conducting an analysis of cough sounds. We review a range of recent studies that explore the use of deep learning models, machine learning algorithms, and feature extraction techniques in this context. These studies are categorized based on their methodologies, and their performance in accurately classifying lung sounds is discussed. Research findings highlight the significant diagnostic potential of lung sound analysis, demonstrating improved classification accuracies across various disease types. However, challenges such as data inconsistency, noise interference, and limited model generalizability remain. It captures the current state of lung sound analysis research in respiratory disease detection, offering insights that could drive future advancements in this critical field.

关键词： Machine learning algorithms Accuracy Tuberculosis Lungs Pulmonary diseases Medical services User interfaces Data models Convolutional neural networks Mel frequency cepstral coefficient

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Efficient Traffic Density Calculation for Diverse Road Traffic using Yolov8 Algorithm

Efficient Traffic Density Calculation for Diverse Road Traff...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Kotla Amulya Mandarapu Sahithya Mulka Trisha Shanmugasundaram Hariharan A V Senthil Kumar R. Karthikeyan Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of MCA Hindusthan College of Arts & Science Coimbatore Tamilnadu India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Accurate traffic density estimation is vital for effective traffic management. Traditional methods like inductive loop detectors and radar have high costs and scalability issues. This report introduces a method using the YOLO (You Only Look Once) object detection algorithm to calculate traffic density. YOLOv3, fine-tuned with a custom dataset, enhances vehicle detection accuracy. The approach involves collecting video feeds from traffic surveillance cameras, sampling frames at regular intervals, and processing them with YOLOv3 to detect and count the number of vehicles. Traffic density is calculated based on vehicles per unit length of the road. Testing across various lighting conditions and traffic densities showed a detection rate consistently above 95%, validating the method's robustness. YOLO's real-time processing ensures efficient traffic density computation, supporting timely traffic control *** algorithm is also detects the vehicles but not accurate as YOLO algorithm as it does not detect the fast moving vehicles which leads to the inefficient use of algorithm.

关键词： YOLO computer vision Accuracy Vehicle detection Surveillance Urban planning Traffic control Cameras Real-time systems Testing

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Recognition and Identification of COVID-19 from Chest X-Rays for Earlier Diagnosis 4

Recognition and Identification of COVID-19 from Chest X-Rays...

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

作者： Kavitha, P. Sharma, Manish Pradeep Kumar, G.M. Barve, Amit Patil, Harshal Maranan, Ramya CMR Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560037 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India BMS Institute of Technology and Management Department of AI & ML Karnataka Bangalore560064 India Parul Institute of Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering P.O. Limda Gujarat 391760 India Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

Early detection is critical for controlling the spread and death of the COVID-19 worldwide pandemic, which affects global health care and lifestyle. The gold standard diagnosis, Reverse Transcription Polymerase Chain Reaction (RT-PCR) is both costly and has a lengthy process time. So more readily available and fast diagnostic methods are necessary. Many researchers proved that COVID-19 data can be effectively detected by Chest X-rays (CXR). However, diagnosing chest X-ray images for COVID-19 is difficult and error-prone. The only way to improve the COVID-19 diagnosis is to use DL algorithms. In this research, a novel deep learning model Convolutional Neural Network and Autoencoder (CNN-AE) tuned by Bat Optimization Algorithm (BOA) is proposed. The proposed approach would identify whether the CXR images were positive or negative for COVID. We received the CXR images from Kaggle. The pre-processing phases in the proposed technique assist with providing relevant information and reducing biased outputs. This is followed by the classification model. We were able to evaluate the performance of our proposed model by using Kaggle datasets. Our proposed model is evaluated using Kaggle datasets and achieved an average accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, F1-score, False Positive Rate (FPR), and False Negative Rate (FNR) of 99.28%, 99.42%, 99.14%, 99.42%, 99.28%, 0.85%, and 0.57% respectively. The outcome of the proposed method is compared with the existing and similar research. A comparative study shows that our proposed method outperforms the state-of-art methods. © 2024 IEEE.

关键词： COVID-19

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Cyber-attack Detection Using Gradient Clipping Long Short-Term Memory Networks in Internet of Things 1

Cyber-attack Detection Using Gradient Clipping Long Short-Te...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Ayyalasomayajula, Madan Mohan Tito Mandala, Vishwanadham Prasad, Hanumat Gangopadhyay, Amit Shrivastava, Neeraj Sundaram, Ajith Aspen University School of Business & Technology Department of Computer Science Arizona United States Cummins Inc. IN United States Department of CSE- AI&ML Guntur India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Tirupati India Medicaps University Indore India Amrita Vishwa Vidyapeetham Amrita Nagar Edappally Amrita School of Business Kochi Kerala Ernakulam India

ISBN: (纸本)9798350367003

The Internet of Things (IoT) is a network that connects a vast number of objects, enabling them to communicate and interact with each other with human intervention. The IoT is seeing rapid growth in the field of computing. However, it is important to acknowledge that IoT is very susceptible to many forms of assaults due to the hostile nature of the internet. In order to address this problem, it is necessary to implement practical steps to ensure the security of IoT networks, such as the implementation of network anomaly detection. While it is impossible to completely prevent assaults indefinitely, timely discovery of an attack is essential for effective defense. Because IoT devices have limited storage and processing power, standard high-end security solutions cannot protect them. In addition, IoT devices are now autonomously linked for extended durations. Consequently, it is necessary to create advanced network-based security solutions such as deep neural network solutions. While several researches have focused on the use of neural network methods for attack detection, there has been less emphasis on detecting assaults, especially in IoT networks. The objective of this research is to develop a gradient clipping long shortterm memory network (GC-LSTM) that can efficiently and promptly identify IoT network assaults. The Bot-IoT dataset is employed for evaluating various detection methodologies. The incorporation of additional features resulted in improved results. The GC-LSTM model, as proposed, achieves a remarkable accuracy of 99.98%, 97.67% of detection rate, 87.34% of TNR and 34.67% of FAR. © 2024 IEEE.

关键词： Network intrusion

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