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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mishra, Vishnu Kumar Mishra, Megha Tiwari, Rajesh Kumar, T. Ram Diwan, Tarun Dhar Patra, Raj Kumar Malla Reddy Engineering college for Women Dept. of Computer Science and Engineering Hyderabad India Csvtu Shri Shankaracharya Technical Campus Department of Computer Science and Engineering Bhilai India CMR Engineering College Department of Computer Science and Engineering Hyderabad India Atal Bihari Vajpayee University Dept. of Computer Science and CoE Chhattisgarh Bilaspur India

ISBN: (纸本)9798331505790

The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this study to solve the problems related to precise and timely monitoring of vehicle movement. By improving the effectiveness and dependability of current systems, the suggested approach hopes to promote traffic management for smart transportation. © 2024 IEEE.

关键词： Convolutional neural networks

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Contribution of Algorithm Visualizations to Students' Learning Skills: A Pedagogical Approach 12

Contribution of Algorithm Visualizations to Students' Learni...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kheder, Harem Ali Varol, Asaf College of Engineering and Computer Sciences Lebanese French University Dept. of Computer Engineering Erbil Iraq College of Engineering and Computer Science The University of Tennessee at Chattanooga Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350330366

The Domain Algorithm visualizations are multimedia-based representations of algorithms that help students learn how they operate and behave. Algorithm visualizations provide numerous benefits to instructors and students, including an improved understanding of difficult algorithms in programming. By stripping down the complexities of algorithms and presenting them in simplified visual ways, students can obtain a more thorough knowledge of how they perform. Instructional approaches, such as guided instruction or discovery learning, are critical to the pedagogical effectiveness of algorithm representations. Guided instructions can considerably boost learners' overall performance in problem-solving activities that incorporate visualizations. At the same time, discovery education promotes exploration and experimentation, yet it may result in longer learning times and increased mental demands. A combination of these approaches is required for optimal effectiveness of instruction. In this paper, we investigate how the pedagogical effectiveness of algorithm visualizations should be determined by a variety of elements, including student characteristics, visual and cognitive processing ability, algorithm representation, and instructional context based on a literature review and interviews with field experts, and a bunch of recommendations is provided that has witnessed by authors during their long teaching periods. © 2024 IEEE.

关键词： Students

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Speech Emotion Recognition Using ML 7

Speech Emotion Recognition Using ML

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Niharika, S.M. Soumya, A. Rv College of Engineering Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350343144

Due to its numerous uses in industries including human-computer interaction, healthcare, and entertainment, speech emotion recognition (SER) has attracted a lot of interest recently. The use of Convolutional Neural Networks (CNN) in combination with the Librosa library for SER is used to recognise emotions from audio data. The capacity to automatically recognise and categorise emotions from spoken words can revolutionise many applications since emotions are a crucial component of human communication. Here, CNNs-known for their skill in image analysis-are adapted to the spectrogram representation of audio, efficiently identifying distinguishing characteristics. To improve the reliability of emotion identification, Librosa, a Python library for music and audio analysis, offers crucial capabilities for preprocessing and feature extraction from audio data. The architecture of the CNN model, which includes of convolutional layers for feature extraction and pooling layers for dimensionality reduction, is required to build the model. To increase model generality, methods for data augmentation and transfer learning are employed. The two feature extraction capabilities of Librosa, chroma features and mel-frequency cepstral coefficients (MFCCs), are emphasised for their effectiveness in extracting emotional cues from audio data. It has been proved via considerable research that CNNs and Librosa together produce state-of-the-art results in a variety of emotion identification tasks, considerably improving SER accuracy. This study has broad repercussions, from enhancing the emotional intelligence of virtual assistants to helping mental health practitioners monitor patients' emotional health through speech analysis. In summary, the combination of CNNs and Librosa in SER offers a potential path for developing the discipline of emotional analysis in speech, resulting in more emotionally intelligent technology and a deeper comprehension of human emotions. © 2023 IEEE.

关键词： Feature extraction

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Analysis of the Surface Water Quality in the State of Karnataka using Distributed Platform 9

Analysis of the Surface Water Quality in the State of Karnat...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Dasu, Shravya Shobha, G. Shetty, Jyothi RV College of Engineering Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Water, one of the essential substances for life on earth is crucial for humans, plants and animals. Regular testing and monitoring of water quality is essential to ensure its safety for consumption. This process includes measuring physical and chemical parameters such as PH, total dissolved solids (TDS) etc. Herein, surface water quality in Karnataka, India was analyzed over 40 years. In order to analyze the large amount of data collected, high performance computing cluster (HPCC) systems was used. Biochemical oxygen demand (BOD) predictive model was built using linear regression. Additionally, a potability classification model was built using the random forest classifier algorithm. These algorithms are present in the LinearRegression and LearningTrees bundle provided by HPCC Systems. Finally, a graphical visualization of water quality in four basins over time was performed with the Visualizer bundle. This was done to understand the improvement or degradation of water quality. The analysis was performed on a single node and an 8-node cluster respectively. In comparison with the former, the latter had a shorter execution time. © 2023 IEEE.

关键词： Water quality

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A Survey on Integration of Blockchain in Data Trading Industry 5

A Survey on Integration of Blockchain in Data Trading Indust...

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Shamila Naval, M.K. Thara, R.J. College of Engineering Dept of Computer Science and Engineering Kerala Trivandrum India

ISBN: (纸本)9798350334128

With the development of computer and information technology, diverse types of data are being created daily. This data along with big data technologies could give a lot of useful information when used right. To make this possible, many data trading platforms exist now. But since these systems are based on a third party platform who collect and trade data, there are many privacy and security concerns. To solve these problems, blockchain technology have been being proposed. The decentralised and transparent nature of blockchain could help to solve many security concerns. In this work, we provide a survey on data trading systems which utilize blockchain as the primary entity for trust-enabling to avoid any intermediaries. We provide an overview of blockchain technology and discuss the working and limitations of the centralized data trading systems. Also, we provide insights into DApp and Ethereum blockchain. © 2023 IEEE.

关键词： Blockchain

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Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Comparison of Faster RCNN and YOLO V3 for Video Anomaly Loca...

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2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Anoopa, S. Salim, A. Nadera Beevi, S. College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala India Government Engineering College Thrissur Dept. of Computer Science and Engineering Kerala India Tkm College of Engineering and Technology Dept. of Computer Applications Kerala India

ISBN: (纸本)9798350334463

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Convolutional neural networks

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Secure Forgery Detection in Android Malware Applications with Trust Analysis in Google Play 3

Secure Forgery Detection in Android Malware Applications wit...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Sargunan, P. Yasodhei S, Umadevi Ifet College of Engineering Dept of Computer Science Engineering Villupuram India

ISBN: (纸本)9798350322842

With the widespread use of Android devices, the threat of malware applications has become a serious concern for both individuals and organizations. This research study proposes a novel methodology for safe forgery detection in malware applications for Android using machine learning and behavioral analysis techniques. The proposed methodology involves feature set extraction, pre-processing, and classification steps to accurately detect Android malware applications while minimizing false positives. Experimental results demonstrate that the proposed methodology outperforms existing state-of-the-art techniques in terms of accuracy, precision, recall, and F1 score. The methodology was also able to accurately detect a wide range of malware types, including previously unknown or zero-day malware. Our study highlights the importance of using advanced technologies and innovative approaches to detect and prevent Android malware, and suggests that future research could investigate additional machine learning algorithms and behavioral analysis techniques to improve detection accuracy. As the prevalence and sophistication of Android malware continue to increase, it is critical that researchers and practitioners work together to develop effective and reliable methods for detecting and preventing these threats. By doing so, users and organizations can be protected from the harmful effects of Android malware, and ensure the ongoing security and integrity of the Android ecosystem. © 2023 IEEE.

关键词： Feature extraction

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BWFSO: Hybrid Black-widow and Fish swarm optimization Algorithm for resource allocation and task scheduling in cloud computing

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Materials Today: Proceedings 2022年 62卷 4903-4908页

作者： Manikandan, N. Divya, P. Janani, S. Dept of Computer Science Engineering Saveetha School of Engineering Chennai 600059 India Dept of Computer Science St. Joseph's College of Engineering Chennai 600024 India Dept of Computer Science of Engineering St. Joseph's College of Engineering Chennai 600119 India

Cloud computing has been booming technology in recent years. It is used to share resources over the internet. Though cloud has many advantages and is used worldwide. It also has some disadvantages and issues. The major problem in cloud computing is scheduling and resource allocation. Allocating resources and tasks is one of the highly critical challenges. There are no proper methods or techniques to improve task scheduling and resource allocation. Previous methods used Virtual machine (VM) instances for scheduling. The major drawback of using Virtual machine instances is that it takes a lot of startup time and consumes all the resources to perform the task. In this paper, we have proposed a solution with fuzzy C-means clustering hybrid algorithms of using Black widow optimization for task scheduling and fish swarm optimization for efficient resource allocation to reduce cost, energy, resource utilization. © 2022

关键词： Resource allocation

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Melanoma Detection Using Deep Learning

Melanoma Detection Using Deep Learning

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Venkatesh, B. Annapoorna, S. Das, Atoshi Renusree, P. Sindhuja, R. Dept of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (纸本)9798331517953

Melanoma is the deadliest type of skin cancer, so early detection is essential and requires accurate identification. With performance on par with dermatologists, Convolutional Neural Network (CNN) based classifiers have become the go- to approach for melanoma identification. In this paper, the use of the VGG-16 convolutional neural network architecture for melanoma detection in dermatoscopic images is investigated. Class imbalance and image variances are addressed by fine- tuning the model using a diverse dataset by utilizing VGG- 16's deep hierarchical feature extraction capabilities. The model is tuned for accuracy and generalization by leveraging aug- mentation and transfer learning approaches. Training entails optimizing hyperparameters and evaluating them on an unknown dataset using quantitative performance metrics such as F1 score, Precision, Recall, and accuracy. VGG16 CNN model achieved an 88.60% of accuracy over other models. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model 1

Automated Identification of Pneumonia Employing Convolution ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Anamika Gupta, Anuj Kaushik, Deepti Gupta, Neha Chadha, Shikha Chauhan, Rosey Noida Institute Of Engineering Technology Dept. of Computer Science & Engineering Gerater Noida India Sharda Unviersity Dept. of Computer Science & Engineering Gerater Noida India School of Engineering and Technology IIMT University Dept. of Computer Science and Engineering U.P Meerut India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned. © 2024 IEEE.

关键词： Convolutional neural networks

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