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4th International Conference for Emerging Technology, INCET 2023

作者： Yadav, Vidushi Pangaonkar, Satyajit Gunjan, Reena Rokade, Prakash Avantika University School of Computer Science and Engineering Madhya Pradesh Ujjain India Design and Technology University MIT School of Computing MIT Art Maharashtra Pune India Cloud and Infrastructure Services Infosys Ltd Maharashtra Pune India

ISBN: (纸本)9798350335750

Crop infection in terms of bacteria, fungus and other viruses is an unrelenting issue and loos of food due to this is a key challenge faced by farmers for many decades. Proper identification of this disease may prevent this loss. This study describes precision agriculture in terms of a mobile application called 'plant pathologist' for detecting plant diseases that forecast their occurrence using machine learning and artificial intelligence. A combination of computer vision and deep learning algorithms were used to evaluate images of plant leaves and compared them to a database of known diseases in order to make a quick and accurate diagnosis. The development process involved the identification of the research issue and the objectives, the gathering and pre-processing of data, the choice and training of the model, and the model evaluation through this quantitative methodology. The convolutional neural network (CNN) was trained on a sizable dataset of annotated plant disease photos as part of the machine learning model utilized in the mobile app. The CNN was trained to recognise the visual characteristics of photos of healthy and diseased plants and classify them according to several disease types. To ensure adaptability and precise identification of plant diseases, the app has been trained on a specific type. With hyperparameter tuning and cross-validation, the best-performing CNN model was chosen for the model selection procedure. The mobile application, which offers thorough information on plant disease diagnosis is then updated with the chosen model. This offers farmers, botanists, and plant enthusiasts an invaluable resource to accurately identify plant diseases through machine learning. © 2023 IEEE.

关键词： Convolutional neural networks

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A Survey on a Novel Cryptojacking Covert Attack 2

A Survey on a Novel Cryptojacking Covert Attack

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gajmal, Yogesh M. More, Pranav Kale, Kiran Dhanaji Jagtap, Arvind Finolex Academy of Management and Technology Information Technology Ratnagiri India School of AI & Future Technologies Universal AI University Karjat India Presidency University Bangalore Electronics and Communication Engineering Bengalore India MIT School of Computing MIT Art Design and Technology University Pune India

ISBN: (纸本)9798350327533

New technologies based on cryptocurrencies and blockchain are revolutionizing how we conduct commercials online. Nowadays, a wide range of blockchain as well as cryptocurrency systems, apps, also technologies are publicly accessible in the direction of businesses, end users, as well as even malicious attackers that seek towards deploy cryptojacking malware to abuse the computational resources of common people. Without the victims' knowledge, this type of malware operates on their machines. In order to mine cryptocurrency for the cybercriminal, it frequently infects browsers and performs CPU-intensive computations. The cybercriminal then steals the revenues without compensating for the resources used. However, current detection techniques, for instance browser extensions that designed to defend users using blacklist protection or antivirus programmes by different analysis techniques, can only partially solve the problem of emerging cryptojacking because attackers can simply get around them via using obfuscation methods or else frequently altering their domains or else scripts. As a result, numerous researches in the literature suggested employing different dynamic/behavioral indicators to identify cryptojacking malware. However, a systematic study using a deep grasp of the new cryptojacking attack as well as a thorough assessment of studies into the literature is lacking in the literature. Therefore, using data from the web and journal articles, we give a systematic study of cryptojacking in this paper. Finally, we offer some prevention advice as well as warning signs that you might have been a victim of cryptojacking. © 2024 IEEE.

关键词： Bitcoin Blockchain Cryptocurrency Cryptojacking Malware Cryptomining

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IoT-enabled Student Attendance System using RISC-V based Embedded System

IoT-enabled Student Attendance System using RISC-V based Emb...

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Learning and Technology Conference (L&T)

作者： Khadija Khan Nada Alzahrani Reham Abed Iman Sofia Razi Imed Ben Dhaou Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Inefficiencies, cumbersome processes, and a significant risk of errors, delays, and misuse often plague conventional methods for tracking student attendance. To address these challenges, this paper introduces a smart monitoring system driven by the Artificial Intelligence of Things (AIoT), specifically designed for resource-limited embedded processors. This research examines a real-time facial recognition-based student attendance tracking system implemented on an edge device. Additionally, it assesses the effectiveness of ARM and RISC-V microprocessors as the foundation for a reliable edge computing platform. The system employs Python’s Dlib library and the ResNet model for effective facial recognition. Comparisons between the Raspberry Pi Model 4 B and the RISC-V StarFive VisionFive2 are made to determine the optimal platform for extensive deployments. The proposed edge system emphasizes security, automation, and reliability, facilitating real-time, precise attendance tracking and significantly improving operational efficiency for educators and administrators. The solution using the RISC-V processor is at least 10 times more energy-efficient than the competing solution based on the RISC processor instruction set architecture.

关键词： Program processors Reduced instruction set computing Embedded systems Face recognition Computational modeling Real-time systems Internet of Things Security Reliability Artificial intelligence

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Deep learning

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Adapting Deep Learning Transformer Model for Depression-Specific Named Entity Recognition: A Transfer Learning Approach

Adapting Deep Learning Transformer Model for Depression-Spec...

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

作者： Dipti Pawar Shraddha Phansalkar Department of Computer Science Engineering MIT School of Computing MIT Art Design Technology University Pune India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Named Entity Recognition (NER) is essential in the biomedical domain, particularly in mental health studies focused on disorders like depression. It helps extract structured information from unstructured text, enabling accurate recognition of key entities such as symptoms, disorder, antidepressants, brands, and therapies. This step is crucial for recommending diagnoses, personalizing treatments, and advancing research. Traditional methods, like rule-based or dictionary-driven approaches, often fall short in flexibility and scalability, as they rely heavily on predefined lexicons and corpora. The proposed work presents an advanced deep learning model aimed at improving NER performance on a customized dataset. Our model focuses on identifying critical entities disorders, brands, antidepressants, and therapies by addressing the limitations of conventional techniques. It uses a hybrid approach that combines a BERT-based model, fine-tuned through transfer learning, with a knowledge-based dictionary (MedDRA). This integration allows for enhanced semantic enrichment, improving the identification and categorization of biomedical entities. By leveraging BERT’s contextual capabilities alongside the structured knowledge from MedDRA, the model delivers better accuracy and classification in mental health data. Preliminary results show high confidence levels, with substantial improvements in key metrics like precision, recall, and F1 score, further advancing NER's role in mental health research.

关键词： Deep learning Adaptation models Accuracy Biological system modeling Computational modeling Transfer learning Antidepressants Medical treatment Mental health Named entity recognition

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A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Pankaj Chandre Manoj Bapurao Shinde Shruti Vilas Tilwant Arun Bhau Ghandat Bhagyashree D Shendkar Prashant Dhotre Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience.

关键词： Adaptation models Q-learning Prevention and mitigation Intrusion detection Machine learning Network security Feature extraction Real-time systems Monitoring Resilience

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Adaptive Behavioral Authentication for Fraud Detection: Leveraging Real-Time User Behavior to Enhance Financial Security

Adaptive Behavioral Authentication for Fraud Detection: Leve...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Pankaj Chandre Smita Gumaste Aditi Wangikar Suruchi Deshmukh Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Pune India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This paper presents a comprehensive analysis of adaptive behavioral authentication systems designed for fraud detection in financial services. These systems leverage real-time user behavior, such as typing patterns, mouse movements, and geolocation data, to continuously monitor and assess authentication risks. A layered approach integrates behavioral analysis with traditional credentials, providing enhanced security against evolving fraud techniques. The proposed system illustrates the interaction between users, the authentication system, a behavioral engine, and fraud detection models, enabling dynamic decision-making processes. The proposed framework enhances fraud detection by ensuring robust monitoring without compromising user experience. Future work aims to address challenges in data privacy, ethical considerations, and system adaptability to emerging financial technologies like decentralized finance (DeFi).

关键词： Ethics Adaptive systems Geology Authentication Finance Real-time systems User experience Fraud Security Monitoring

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Research on PCB tiny displacement estimation method for ORB feature-guided FREAK descriptor enhancement 4

Research on PCB tiny displacement estimation method for ORB ...

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Yang, Duo Su, Yang Wu, Hongwei Yi, Pengfei Key Laboratory for Advanced Design and Intelligent Computing Ministry of Education School of Mechanical Engineering and Software Engineering Dalian University Dalian116622 China Olympic Circuit Technology Co. Ltd. Guangdong Jiangmen529700 China Co. Ltd. Dalian116622 China

Given the hurdles posed by existing techniques for identifying microscopic offsets in miniature printed circuit boards (PCBs), including their low accuracy and high iteration count, this study presents an innovative approach that measures micrometer offsets by utilizing the ORB feature-guided FREAK descriptor enhancement. It improves matching outcomes with a brute-force matcher and RANSAC via ORB feature extraction to guide FREAK descriptors for better feature uniqueness. A PCB small offset detection algorithm is developed with a homography matrix and a theoretical offset model. A novel distance threshold performance evaluation method for this suggested matching algorithm that objectively evaluates matching results is also proposed in this article. The proposed matching method outperforms alternative algorithms in precision and recall, according to its experiments. This research conducts simulation detection of PCB calibration bare boards and instance verification of real PCB boards by constructing a PCB tiny offset detection experimental system. The results show that micron-level detection precision can be achieved without iteration. © Published under licence by IOP Publishing Ltd.

关键词： engineering education

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Enhanced Food Classification System Using YOLO Models For Object Detection Algorithm 14

Enhanced Food Classification System Using YOLO Models For Ob...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sudharson, S. Kokil, Priyanka Annamalai, R. Sai Manoj, N.V. Computer Science and Engineering Vellore Institute of Technology Chennai India Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350335095

Food classification is crucial for the food industry and has a significant impact on public health and nutrition. Deep learning techniques such as YOLOv5 and YOLOv7 have recently shown significant improvement in food categorization accuracy. The results of this study's analysis of both models is evaluated on a manually labeled dataset, evaluating their accuracy, recall, and mAP at the different intersections over union (IoU) criteria. The accuracy, recall, and mAP of both YOLOv5 and YOLOv7 were evaluated at various IoU criteria, and the results showed that YOLOv5 outperformed YOLOv7 in all three metrics. Specifically, YOLOv5 achieved an accuracy score of 0.851 and a recall score of 0.836, whereas YOLOv7 scored 0.67 and 0.675, respectively. Furthermore, YOLOv5's mAP score at the 0.5 IoU threshold was 0.892, while YOLOv7 scored 0.669. At the 0.5-0.95 IoU threshold, YOLOv5's mAP was 0.644 and YOLOv7's was 0.426. This study demonstrates that YOLOv5 is a superior deep learning model for food categorization tasks, with potential implications for improving consumer nutrition and food safety in the food industry. © 2023 IEEE.

关键词： Object detection

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Classification and Segmentation of Brain MR Images Using Deep Neural Networks and Fuzzy C-Mean Clustering

Classification and Segmentation of Brain MR Images Using Dee...

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Women in computing (InCoWoCo), International Conference for

作者： Pallavi Asthana Deepika Kamboj CSE Department School of Computing MIT Art Design and Technology University Pune India Multidisciplinary Engineering Department School of Engineering The NorthCap University Gurugram India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Brain tumor arises mainly due to the tissue's abnormal growth inside the brain. They are dangerous as compared to other kinds of tumors because of their aggressively fast-spreading nature leading to a lower survival rate in the patients. Therefore, detecting and segmenting brain tumors from brain MR images helps radiologists in the early treatment planning of patients. The proposed work uses deep neural networks to classify and a fuzzy C-mean clustering algorithm to segment. First, classification is done to detect whether the image is normal or has a tumor. Next, segmentation is done for the MR images with tumors. The evaluated results are also compared with the recently published work.

关键词： Image segmentation Visualization Brain tumors Clustering algorithms Artificial neural networks Prediction algorithms Classification algorithms Planning

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