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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2022

作者： Singh, Divjot Mishra, Ashutosh Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9783031151743

The human brain is the most complex part as it controls numerous operations of the human body. For the past few decades, works have been going on to study the working of brain and how to make models that can replicate its working. Artificial intelligence is one such advanced field that trains on data we collect and predicts the output like a human brain. On the other hand, there are many diseases related to brain that have been discovered over time. These diseases progress with the passage of time, and, due to their slow rate of progression, it becomes difficult to detect them at early stage. Alzheimer’s disease (AD) is one of the neurodegenerative diseases which damages the brain permanently and becomes untreatable at the higher stages. Thus, it becomes very important to detect AD at an early stage to increase the life span of the person detected. The main predicament is to detect AD at an early stage and the selection of features responsible for it. The objective of this study is to predict AD at an early stage and to identify the features that facilitate its early prediction using ensemble learning. The proposed multiple ensemble method uses the selected features to achieve an accuracy of 82.40% and with higher ROC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain

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Face Recognition Based on Cascade Classifier Using Deep Learning 1

Face Recognition Based on Cascade Classifier Using Deep Lear...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Mannepalli, Praveen Kumar Singh Kushwaha, Devendra Kalamdhar, Sanjay Nagrale, Vishakha Rajpoot, Vikram GH Raisoni Institute of Engineering and Technology Department of Computer Science & Engineering Nagpur India Madhav Institute of Technology & Science Gwalior India

ISBN: (纸本)9798350345957

The human face is significant because it is used as a tool of identification in our daily lives. One kind of biometric identification is Face Recognition (FR), which works by memorizing and using a person's unique facial features to identify that person in the future. Numerous researchers have been interested in biometric facial recognition technology due to its widespread use. Because of the non-contact nature of the process, FR technology surpasses other biometric-based authentication methods like fingerprint, palm print, & iris recognition. One such potential use for facial recognition (FR) algorithms is in remote identification when no physical touch or interaction with the target individual is required. This study proposes a method for constructing an FR system with a learning strategy depending on the DCNN algorithm. The approach of Deep Learning (DL) is presented for learning & assessing all samples & new inputs in a scheme. The ORL and YALE face datasets are used in studies that compare the findings to those produced by the Deep CNN approach. The proposed DL method uses a systematic approach to learning from and assessing new inputs and samples. The proposed model Deep CNN obtained 96.04 percentage points (or a validation accuracy of 62.2%) over the ORL dataset after 100 iterations with a loss of 17.33 percent. DCNN achieves a 99.99% training accuracy and a 99.89% validation accuracy without any data loss after 100 epochs of training on the Extended Yale B dataset. The proposed DL method analyses and rates are existing data from the system and fresh face inputs. In addition, it gathers information from the facial features & enhances the D-CNN algorithm for facial classification. Utilizing CNN (Convolutional Neural Networks), the accomplishments in several contests are advancing and becoming the focus of study. © 2023 IEEE.

关键词： Face recognition

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Aerial Image Segmentation Using YOLO v8 and SAM Pipeline

Aerial Image Segmentation Using YOLO v8 and SAM Pipeline

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Pai, Karthik K Agarwal, Monika Sinha, Aparajita Jasmine, Mary Jagrath Chengappa, K.N. Bangalore India National Institute of Technology Department of Computer Science and Engineering Agartala India Christ University Department of Computer Science and Engineering Bangalore India Dayananda Sagar University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540821

Aerial image segmentation, important for understanding image content, faces significant challenges such as feature scale variation, resolution issues, spectral inconsistencies, and class imbalance. This paper introduces a robust pipeline that combines YOLOv8 for object detection and the Segment Anything Model (SAM) for refining segmentation. The study addresses detection attributes, augmentation methods, and dataset creation for reliable modelling. Trained on a custom sub-meter aerial image dataset, the model achieves moderate accuracy with MAP@50 of 0.68 and MAP@50-95 of 0.51. It demonstrates strong potential for applications in urban planning, disaster management, and geospatial analysis. By enhancing segmentation accuracy and efficiency through cutting-edge deep learning techniques, this method supports critical geospatial applications. © 2024 IEEE.

关键词： Deep learning

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Adaptive Neuro Transformers for Stroke Lesion Segmentation: GatedNeuroTransUNet Implemented using CNNs with Bottleneck Residual Blocks

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SN computer science 2025年第5期6卷 1-17页

作者： Sulaiman, Sadiya Thanka, M. Roshni Edwin, E. Bijolin Salam, Nader Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

Accurate lesion segmentation is essential for diagnosing and treating strokes. However, the task of segmenting stroke lesions is difficult due to the intricate backgrounds and noise prevalent in medical images. To tackle this challenge, the proposed research introduces an enhanced U-Net architecture integrating pre-trained CNN blocks, bottleneck residuals, and an adaptive gated neuro transformer with a modified MLP. This novel architecture is structured to assimilate both local and global aspects, thereby improving segmentation accuracy. The pre-trained CNN blocks in the architecture take advantage of features learned through supervised tasks, which enhances the model's generalization abilities. Bottleneck residual blocks are incorporated to perform high-level feature extraction and dimensionality reduction, that is essential for managing the complexities inherent in medical imagery. The adaptive gated neuro transformer, equipped with a novel MLP, will efficiently captures long-range correlations by utilizing spatial data, further refining the segmentation process. To confirm our model's efficacy, we conducted trials with the ATLAS and ISLES2022 collections. Observations showed that our model markedly surpassed traditional approaches, highlighted by substantial enhancements in the Dice Similarity Coefficient (DSC) and decreases in the Hausdorff Distance (HD). Such indicators are vital for assessing the precision and reliability of segmentation methods. In addition to its quantitative success, the proposed method excels qualitatively by preserving edges and accurately detecting lesion boundaries. This indicates that the proposed improved U-Net transformer architecture is not only effective in segmenting stroke lesions but also holds considerable promise for clinical application. By enhancing the precision of stroke lesion segmentation, the proposed model is potentially contributing to better diagnosis and treatment planning, ultimately enhancing patient care. © The A

关键词： Bottleneck Residual Blocks CNN GatedNeuroTransUNet Lesion Neuro Transformers Segmentation Stroke lesions

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Smart Trainer using OpenCV

Smart Trainer using OpenCV

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Podduturi, Harsha Vardhan Reddy Varla, Chandrashekar Gopaldinne, Koushik Reddy Bhukya, Naresh Reddy, Revanth Kumar Bapiraju, G.S. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350397208

Exercise is beneficial to human health;however, if performed incorrectly, it becomes ineffective or even harmful. Obesity mostly occurs in people who do not exercise. Current research demonstrates the importance of exercise and considers it as a goal for a healthy lifestyle. Users can be encouraged to exercise correctly with the help of a fitness trainer;however, performing exercises incorrectly without the trainer's direction can be fatal;several complications, such as muscular soreness and cramps, can occur while exercising without supervision. However, when it comes to the trainer, it is very expensive to practice with a skilled fitness trainer. Therefore, this article has attempted to design an Artificial Intelligence (AI) based virtual trainer, a tool that analyzes a user's workout posture and provides individualize and in-depth advice on the ways to correct training and exercising mistakes. © 2023 IEEE.

关键词： AI trainer application exercises supervision training

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NLP based Analysis and Detection of Unethical Text 2

NLP based Analysis and Detection of Unethical Text

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2nd International Conference on Sustainable Computing and Data Communication systems, ICSCDS 2023

作者： Khandekar, Apurva Hema, Chekuri Devi Meghana, Alle Mounika, Akuraju Vaishnavi, V.S. Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9781665491990

In the present scenario, the internet has enabled everyone to express their opinion. As every coin has two sides, it paved a way for increased negativity and intolerance. In this context, unethical text includes offensive comments and hate speech targeting a group or an individual based on their characteristics such as race, religion or gender and that may threaten social peace. So, the unethical text should be eliminated before it reaches a user where identifying an immoral text plays a pivotal role. Various methods are used in identifying a text, one such method, widely known as Natural Language Processing (NLP) is used to perform text analysis in the process of detecting the unethical text. NLP enables us to perform tokenization, embedding matrix, padding and identification of semantic relationships, which helps to analyse the text data efficiently. Different algorithms were used to detect the unethical text, this study has used ISTM and Bi-LS TM to identify whether the user's statement is offensive or not. The better and accurate model is considered and a web page is built on it. It predicts the offensiveness of text present in user input. In this way, the proposed model detects the unethical text on the web, which can be used to eliminate unfiltered comments. The best model is achieved by using the Bi-ISTM with an accuracy of 86.4%. © 2023 IEEE.

关键词： Long short-term memory

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Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 8587-8603页

作者： Sakthimohan, M. Deny, J. Elizabeth Rani, G. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Department of Computer Science and Engineering Kalaignar Karunanidhi Institute of Technology Tamil Nadu Coimbatore India

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The

关键词： Intrusion detection

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Proximal Femur Segmentation from the Fluoroscopy Image Using DeeplabV3+ Model 2

Proximal Femur Segmentation from the Fluoroscopy Image Using...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Naik, Niva Mehta, Mayuri A. Joshi, Vivek Sardar Vallabhbhai National Institute of Technology Computer Science and Engineering Surat India Sarvajanik College of Engineering and Technology Computer Engineering Department Surat India R.N.G.Patel Institute of Technology Information Technology Bardoli India

ISBN: (纸本)9798350366570

Segmenting the proximal femur from a fluoroscopy image is crucial for precise surgical treatment of proximal femur fracture. Accurate segmentation of the proximal femur helps the surgeon to insert screws and metal pins correctly during the treatment of the proximal femur fracture. Traditional machine learning-based segmentation methods have limitations in precisely capturing the complex and variable anatomy of the proximal femur. Deep learning-based segmentation methods offer promising solutions to overcome the limitations of machine learning-based methods. However, they struggle with problems like hyperparameter tuning and spatial context handling. Hence, this paper proposes a novel proximal femur segmentation approach utilizing encoder-decoder-based architecture. The proposed segmentation approach has been evaluated utilizing three encoder-decoder-based architectures, namely U-net, U-net++, and DeeplabV3+. Its performance is assessed using four metrics such as dice coefficient, Intersection over Union (IoU), precision, and recall. Among the three encoder-decoder-based architectures, DeeplabV3+ outperforms the U-net and U-net++. DeeplabV3+ yields a Dice coefficient of 0.9566 and an IoU of 0.9171 on the training dataset, surpassing the performance of U-net and U-net++. © 2024 IEEE.

关键词： Deep learning

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Resolving Attached USBs: Analysis of Windows 11 Artifacts 2

Resolving Attached USBs: Analysis of Windows 11 Artifacts

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Budhrani, Akash Singh, Upasna Singh, Bhupendra Defence Institute of Advanced Technology School of Computer Engineering and Mathematical Science Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

The Windows Operating System is known for its convenience which tends to breed more and more user information in form of Artifacts. Artifacts are important repository of potential evidence while conducting any computer investigation. This paper thus provides an analysis of artifacts that will bring out the inside of USB devices attached or mounted on a particular system. Moreover, this paper will parse the data out into usable information and helps to interpret the findings of forensic examiner. In this work, Image file of the suspected system has been exported from the system for further analysis to bring out relevant information. © 2023 IEEE.

关键词： Windows operating system

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Pesticide Usage Forecasting for Agriculture using Prophet Model 5

Pesticide Usage Forecasting for Agriculture using Prophet Mo...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Wani, Afshan Hassan Moharana, Bhimasen Samant, Tapaswini De, Utpal Chandra Dey, Snehasis Banerjee, Shobhan Department of Computer Science and Engineering Lovely Professional University Punjab India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India College of Engineering Bhubaneswar India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350361155

Agriculture plays a vital role in our day-to-day life for the life sustenance of not just human beings but also various domestic and livestock animals. In addition to this, agriculture also contributes significantly to the economy of a country, impacting its growth across the globe. India had always been an agriculture-dominated country before the companies from the West started investing in India. But alongside agriculture come various challenges such as infestation by insects or weeds which eventually lead to the damage of crops and even diseases. But pesticides can even contribute to the contamination of soil, water, and other vegetation in areas where they are been used. In this paper, we have used the Prophet model to study the use of pesticides and analyze the trends of usage of organic pesticides over inorganic pesticides. © 2024 IEEE.

关键词： Pesticides

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