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Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

computer Modeling in engineering & sciences 2024年第8期140卷 1507-1537页

作者： Haijian Shao Suqin Lei Chenxu Yan Xing Deng Yunsong Qi School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA Zhejiang Geely Automobile Research Institute Ningbo315336China

This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance *** methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging *** envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these *** enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image ***,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent *** utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic *** computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition *** validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced *** refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original *** Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX *** envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of lumi

关键词： Target detection extremely low-light wavelet transformation highly differentiated features YOLOX

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Optimizing Energy Usage in Educational Environments through the Internet of Things and Deep Learning Techniques 3

Optimizing Energy Usage in Educational Environments through ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Preethi Salian, K. Salian, Supriya Rao, Abhishek S. Nmam Institute of Technology Department of Information Science and Engineering Nitte India St. Joseph Engineering College Vamanjoor Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350369083

Excessive electricity consumption in educational environments, particularly classrooms, poses significant financial and environmental challenges. Existing solutions utilize various technologies such as surveillance cameras, power monitoring modules, and occupancy sensing systems to detect occupancy and control electrical appliances accordingly. However, the inefficient management of energy resources within classrooms leads to unnecessary consumption and increased costs. Artificial Intelligence (AI) can assist by implementing smart systems that use IoT devices and deep learning algorithms to autonomously detect occupancy and manage electrical appliances. This approach optimizes energy usage, reduces wastage, and leads to more sustainable and cost-effective educational facilities. The proposed study addresses this pressing issue through the integration of IoT technology and deep learning algorithms to autonomously identify occupancy status within classrooms. By leveraging this data, the system optimizes energy usage by controlling electrical appliances, thereby mitigating unnecessary wastage, and promoting sustainable practices within educational institutions. The study assesses existing energy-saving methods in classrooms and identifies their limitations. A novel solution is proposed that utilizes surveillance cameras for occupancy detection, streamlining energy management without additional hardware costs. The proposed model demonstrated 92% accuracy when compared with the state-of-the-art architecture YOLOv3 with SSH, representing conclusive outcomes derived from its operational processes, delineated both qualitatively and quantitatively. © 2024 IEEE.

关键词： Costs

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Corpus Creation and Annotating Multilingual Code-Mixed Kannada English Data with Precise Labels for Depression Detection 15

Corpus Creation and Annotating Multilingual Code-Mixed Kanna...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shwetha, C.H. Pushpalatha, K. Mangalore Institue of Technology & Engineering Vtu Department of Computer Science and Engineering Moodabidre India Vtu Mangalore India

ISBN: (纸本)9798350370249

This study focuses on understanding mental health in multilingual communities. Specifically, it looks at how people make use of a multilingual language like Kannada and English while sharing their emotions and when talking about depression. The study creates a dataset with detailed labels to identify signs of depression and assess its severity. Through the examination of linguistic elements associated with depression, such as emotional expression and grammatical composition, this research contributes to a deeper understanding of how psychological wellbeing is conveyed across various languages. The labeled dataset plays a central role in developing deep learning algorithms capable of identifying and assessing depression. Additionally, the study emphasizes how language matters in mental health discussions and can influence clinical practices in diverse societies. © 2024 IEEE.

关键词： Annotation Code-mixed Deep Learning Depression English Kannada Labeled Dataset Multilingual NLP

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Comparative Study of Different Document Similarity Measures and Models 7th

Comparative Study of Different Document Similarity Measures ...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Singh, Anshika Garg, Sharvan Kumar Department of Computer Science and Engineering Subharti Institute of Technology and Engineering Swami Vivekanand Subharti University Meerut India

ISBN: (纸本)9789819995615

Document similarity refers to an approach of measuring how two or more documents look alike in terms of their content or structure. Document similarity algorithms are used to determine the degree of resemblance or relatedness between various documents. Document similarity plays a pivotal role in a wide range of tasks involving natural language processing, information retrieval, recommender systems and duplicates detection. In this paper, we will be studying and compare the similarity score of documents using different document similarity measures and models like cosine similarity, Euclidean distance, Jaccard similarity, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERTs), etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recommender systems

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Enhancing Accuracy with Recursive Feature Selection Using Multiple Machine Learning and Deep Learning Techniques on NSL-KDD Dataset 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Mohanty, Subrat Kumar, Satendra Agarwal, Mayank Department of Mathematics Indian Institute of Technology Patna India Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

The world has moved toward digital revolution and more and more services are now being available online. This has presented significant challenges in ensuring the availability, integrity, and confidentiality of networks. Therefore, the implementation of intrusion detection systems (IDS) that actively monitor network activity and dynamically analyze incoming traffic has become essential. In modern computer networks, IDS play a critical role in identifying and alerting administrators of any malicious activities that could threaten data *** research aims to evaluate machine learning (ML) and deep learning (DL) techniques for intrusion detection using the NSL-KDD dataset. While having many features may seem advantageous, it doesn’t always lead to improved performance on large datasets. Reducing and picking just the right set of features may improve both speed and accuracy. This is why a method called recursive feature elimination (RFE) is utilized to select the most relevant features for the NSL-KDD dataset. In order to test the efficacy of ML and DL methods in IDS, we have undertaken a comprehensive experiment. Specifically, we tested the effectiveness of several algorithms, including CNN, RNN, LSTM, GB, CART, and CatBoost. Our study involved evaluating the performance of various algorithms and comparing them, and the detection accuracy [lowest-highest] for all the attacks are as follows: DoS [98.79–99.81%], Probe [96.34–99.64%], R2L [96.15–98.66%], U2R [79.98–99.73%]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Comparative Analysis of IoT and Blockchain Technologies in Enhancing Security and Efficiency in Healthcare Systems 4

Comparative Analysis of IoT and Blockchain Technologies in E...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Ashok Shukla, Anshita Thada, Vikas Chole, Vikrant Moolchandani, Jhankar Sahu, Santosh Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper presents an extensive comparison of the IoT and blockchain technologies in the improvement of security, privacy, and interoperability, as well as energy efficiency in healthcare systems. This is because IoT allows for constant tracking and gathering of data from medical devices and this enhances the welfare of the patients as well as the management of the health facilities. However, this has exposed the Internet of Things (loT) technology with multiple risks in data security and privacy since it uses wireless communication and has limited computational power. Such issues can be addressed through the use of blockchain technology because of its distributed and tamper proof ledger. This research work presents a case of how the integration of IoT with blockchain will lead to a middle of the two worlds, where IoT will be useful for handling real time data while blockchain will handle the issue of security. The study concludes that the integration of blockchains with the IoT offers superior results than the individual implementations in several aspects, thus offering a more secure, efficient, and interoperable environment for today's healthcare applications. Possible future work includes improving the integration of these technologies to minimize the energy consumption, improve the scalability of the system and establish feasible implementation models in actual health care systems. © 2024 IEEE.

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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

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SN computer science 2024年第2期5卷 223页

作者： Singh, Onkar Kashyap, Kanchan Lata Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Computing Science Engineering Indian Institute of Information Technology Kottayam Kerala India

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DenseNet201 EfficientNetB6 Histopathology images InceptionResNetV2 Lung cancer MobileNetV2 VGG19

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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Smart Transportation System with Collision Detection and Caution Notification 4th

Smart Transportation System with Collision Detection and Cau...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Rao, Manjula Gururaj Pawar, Sumathi Reddy, K. Hemant Kumar Priyanka, H. NMAM Institute of Technology Nitte Karkala India Department of Computer Science and Engineering VIT AP University Amaravati India Computer Science and Engineering PESU Bengaluru India

ISBN: (纸本)9789819724505

One of the most vital components of a person's life is their transportation system. Today's transportation is increasingly automated. The development of an automated intelligent transportation system (AITS/ITS) is the subject of numerous studies. Because it must take action in relation to the ITS’s visualization and quick action. Developing the ITS is one of the more difficult tasks. Detecting moving and non-moving objects is one of the major components of ITS visualization. Vehicle collision detection and responding appropriately to that is one of the tasks in the visualization of moving things in the ITS. This essay focuses on developing a low-cost forward collision detection warning system that offers a level of safety that may be used during driving of the automobiles. The system measures the distance among moving vehicles that are traveling in a unidirectional and exactly equal path using an ultrasonic sensor and an Arduino controller. It then alerts the driver if they are getting too close to a risky area. Using an audio-visual and ultrasonic warning that measures the distance, the driver and/or any passengers are adequately warned. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ultrasonic sensors

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Creating a Protected Virtual Learning Space: A Comprehensive Strategy for Security and User Experience in Online Education 4th

Creating a Protected Virtual Learning Space: A Comprehensive...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Boddapati, Mohan Sai Dinesh Desamsetti, Sri Aravind Adina, Karunasri Uppalapati, Padma Jyothi Murty, P T Satyanarayana P. B. V, RajaRao Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9783031488870

The pandemic has a significant impact on how people conduct meetings, both in corporations and in schools. Online meetings have become a popular way to connect people from all over the world, lowering the expenses and time associated with travel. Various video conferencing systems and communication tools have aided in this trend towards online meetings. Many countries have moved to online classrooms as an alternative to traditional face-to-face instruction in the educational sector. It has also enabled educational institutions to adapt to changing circumstances and continue to educate students. One of the major concerns is security. As online platforms become more popular, the potential of infiltration activities such as hacking or unauthorized access increases. This study proposes a comprehensive strategy for improving security and user experience in online education. The framework focuses on detecting existing participants, detecting intruders, restricting intruders, and restricting abusive messages. It employs authentication mechanisms, user behaviour analysis, network monitoring, and machine learning algorithms to validate participant identities, differentiate legitimate users from prospective invaders, restrict unauthorized access, and promote courteous conversation. The framework proves its usefulness in minimizing security concerns and promoting a secure online learning environment through simulations and case studies. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Intrusion detection

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