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Marine Life Analysis Based on Ocean Water Level Rise and Climate Change Using Underwater Imaging Techniques

Remote Sensing in Earth Systems sciences 2024年第4期7卷 657-669页

作者： Mate, Anuja Dinesh Arulananth, T.S. Sathiya, T. Jyothi, B. Swarna Raja, A. Wasim Yalawar, Mrutyunjaya S. Computer Technology Sou.Venutai Chavan Polytechnic Pune India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad 500043 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Engineering College Hyderabad India

Forecasting sea levels is crucial for harbour operations and coastal structure design. The oceans make up two-thirds of Earth’s surface;therefore, historically, the marine economy has been extremely diversified as well as abundant. In order to explore as well as harness this difficult environment, prominent businesses and ecological groups are developing and deploying novel gadgets backed by state-of-the-art marine mechatronics solutions. This research proposes a novel method of marine life monitoring with ocean water level rise analysis based on climate changes by underwater imaging using machine learning techniques. Here, the input has been collected as underwater images as well as processed for noise removal and normalisation. Then, these images were segmented for monitoring marine life using U-Net graph cut fuzzy clustering with a spatial neural network. Then this segmented image has been classified using multilayer region-based Q-belief vector neural networks for the analysis of sea level rise analysis based on climate change training. Experimental analysis has been carried out for various marine life monitoring datasets and climate change datasets in terms of training accuracy, specificity, recall, AUC, and Kappa-coefficient. Proposed techniques achieved specificity 93%, training accuracy 91%, AUC 95%, recall 96%, and Kappa-coefficient 90%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate changes Machine learning techniques Marine life monitoring Q-belief vector Water level rise analysis

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing User Experience in AI-Powered Human-computer Communication with Vocal Emotions Identification Using a Novel Deep Learning Method

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computers, Materials & Continua 2025年第2期82卷 2909-2929页

作者： Ahmed Alhussen Arshiya Sajid Ansari Mohammad Sajid Mohammadi Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.

关键词： Human-computer communication(HCC) vocal emotions live vocal artificial intelligence(AI) deep learning(DL) selfish herd optimization-tuned long/short K term memory(SHO-LSTM)

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A comprehensive review on detection of depression through social media analysis 4th

A comprehensive review on detection of depression through so...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Dogra, Shriya Kaur, Parneet Sharma, Tejpal Kumari, Nisha Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781032911571

Social media platforms like Instagram, Twitter, and Facebook have completely changed our world. People today exhibit a kind of digital character and are more linked than ever. While social media undoubtedly offers many noteworthy advantages, there are also unavoidable drawbacks. Later Research has demonstrated a link between elevated use of social media platforms and heightened levels of depression. The goal of the current study is to study the existing machine learning approaches to identify potentially sorrowful Twitter users based on their tweets and network behaviour. To accomplish this, we assessed classifiers that identify whether or not a person is depressed based on information extracted from their tweets and network behaviour. It is observed that as a greater number of features are incorporated, the F-score and accuracy in classifying depressed persons also improve. This technique is a predictive, data-driven tool for early mental health diagnosis of depression or any type of psychiatric ailments. The main contribution of the article is the examination of the traits and how they affect the ability to gauge depression severity. © 2025 the Author(s).

关键词： Tweets

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RRSC-128/64-V1: a two-variant simple, fast, and secure lightweight stream cipher

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International Journal of Information Technology (Singapore) 2025年 1-9页

作者： Chatterjee, Runa Chakraborty, Rajdeep Department of Computer Science and Engineering Netaji Subhash Engineering College West Bengal Kolkata 700152 India Department of Computer Science and Engineering Medi-Caps University Madhya Pradesh Indore India

Lightweight cryptography is now emerging as a new method for providing security to resource-constrained devices. Securing those devices from external hackers during data transmission is now becoming an important issue. This paper proposed a lightweight simple stream cipher which provides security and confidentiality of the data among resource-constrained devices. The proposed cipher comes in two variants: 128-bit and 64-bit based on the key size it used. The user can used any of these two variants as per application type and the features of the resources. Although the stream cipher design is simple but it delivers outstanding hardware performance compared to some well-known stream ciphers like Grain, Trivium, Mickey and two Grain-like stream ciphers Planet and Lizard. It proves its efficiency greater than all except Trivium. It also passed the 15 randomness tests of the National Institute of Standards and Technology (NIST). © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Embedded system FPGA Hardware metrics LFSR Lightweight cryptography Lightweight stream cipher NFSR NIST Nonce Randomness Throughput

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Analyzing Body Language and Facial Expressions Using Machine Learning Techniques

Analyzing Body Language and Facial Expressions Using Machine...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Patel, Harvendra Kumar Rai, Vijendra Singh, Hare Ram Kumar, Rakesh Iimt College of Engineering Greator Department of Computer Science and Engineering Noida India Iec College of Engineering and Technology Greator Department of Computer Science and Engineering Noida India Gniot Noida India

ISBN: (纸本)9798331508685

Body Language decoding is an important aspect of understanding human emotions, intentions, and personality by interpreting non-verbal cu es. It helps to get insights into human behavior, mindset, and psychology of the person. AI Body Talk is a software system that is capable of detecting and interpreting the body language of a person in real-time inference. It can recognize the sign language along with face, hand, and pose estimation with immediate feedback. It empowers the minority group of society who use sign language to communicate in society equipped with the Mediapipe to train custom models with its pre-built solutions to detect face landmarks, hand-tracking, and pose estimation. The results exemplify the system's potential for various applications like monitoring audience distraction, inspecting interviewee personality, understanding cultural sensitivity, diagnosing patients' health, and more. © 2025 IEEE.

关键词： Face recognition

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Analyzing energy efficiency of flying objects in Wireless Sensor and Mobile Ad Hoc Networks 5

Analyzing energy efficiency of flying objects in Wireless Se...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Siddiqui, Shams Tabrez Ahmad, Adeel Ahmad, Jorair Haseebuddin, Mohammad Siddiqui, Alighazi Salim, Abu Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia Jazan University College of Engineering and Computer Science Department of Electrical and Electronics Engineering Jazan Saudi Arabia

ISBN: (纸本)9798350351378

The convergence of flying objects, Mobile Ad Hoc Networks (MANETs), and Wireless Sensor Networks (WSNs) has been made feasible by the widespread proliferation of wireless communication technology. This study delves into the historical development and significant achievements of WSNs and MANETs, with a focus on the incorporation of drones and UAVs to enhance network capabilities. Our main goals involve conducting thorough assessments of communication protocols, routing algorithms, and network architectures in the presence of flying objects. Our particular emphasis is on maximizing energy efficiency. Examining the dynamics of flying objects in WSNs and MANETs, this analysis delves into the various challenges and opportunities that arise. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The study explores existing protocols such as LEACH, AODV and OLSR, highlighting their benefits and challenges in flying objects. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability. © 2024 IEEE.

关键词： Wireless sensor networks

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Improved Brain Tumour Prognosis Using Deep Neural Networks 4

Improved Brain Tumour Prognosis Using Deep Neural Networks

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Vasavi, D. Durgasathvika I, Vindhya VijayChandu, K. Shalini, K. Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Vardhaman College of Engineering Department of Artificial Intelligence and Data science Engineering Hyderabad India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350352931

The segmentation of brain tumor in MRI is an essential step to specify the most likely location, stage and type of a lesion being diagnosed by physician before any medical treatment planning with full-confidence for better prognosis. Traditional manual annotation is expensive, labor-intensive and inconsistent. Deep-learning-oriented image analysis has recently brought new impetus to this quest, as for example in the form of convolutional neural network (CNN) based models which offer some advantages over traditional methods, but may have difficulty in capturing more complex spatial relations. This paper proposes a novel framework for brain tumor segmentation using Dynamic Graph Neural Networks (DGNNs) and Dense Micro-block Difference Feature (DMDF) extraction. Here, DGNNs build and perform operations on graphs which enables them to learn complex spatial as well as contextual features. The DMDF improves feature representation by amplifying regional/micro-block differences on which accurate tumor boundary definition crucially depends. The proposed model integrates DGNNs with a fully convolutional neural network (FCNN), processing multi-modal MRI scans to generate precise segmentation masks. The training pipeline includes robust data preprocessing, normalization, and augmentation techniques for improved generalization. Evaluation on the Brain MRI Images for Brain Tumor Detection dataset demonstrates the effectiveness of this approach, achieving high Dice similarity coefficients and accurate segmentation performance. This method significantly enhances the accuracy and efficiency of brain tumor diagnosis and treatment planning. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing COVID-19 and Pneumonia Detection with a Hybrid ACGAN-CNN Model 15

Enhancing COVID-19 and Pneumonia Detection with a Hybrid ACG...

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

作者： Sasipriyaa, N. Ravikumar, R. Sharmila, C. Gothai, E. Krishnakumar, B. Nithya Sri, R.K. Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India The Erode College of Pharmacy Pharmaceutical Analysis Tamilnadu India SRM IST Trichy Department of Computer Science and Engineering India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350370249

Corona Virus caused a pandemic outbreak all over the world during 2020-2021. Identification of such diseases in the X-ray images needs help of deep learning methodologies involving classifiers. Achieving proficiency in training convolutional neural networks requires a considerable volume of data. Higher accuracy is needed while dealing with medical fieldrelated problems especially. When dealing with small datasets, networks tend to exhibit overfitting problems. In order to get larger dataset, techniques for data augmentation serve to enhance the neural networks' capacity for generalization by expanding existing training data more efficiently. While conventional data augmentation methods do enhance performance, their scope remains restricted. Generative Adversarial Networks (GANs) have proven instrumental in producing fresh data, thereby elevating the performance of CNNs. Methodologies for augmenting data with the help of GAN's is not as much notable in Convolutional neural networks. In this research study, a GAN architecture is used for augmenting X -ray images of chest in order to expand the dataset. Auxiliary Classifier Generative Adversarial Network model has been used to produce images. It consists of a generator that produces new images with random noise and a discriminator whose job is to identify fake & real images. The results showcase that the new image generated is fine-tuned over each epoch and the significance can be seen in the output images. We compare the quality of our ACGAN images with the conventionally augmented images. © 2024 IEEE.

关键词： COVID-19

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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI

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Mathematical Biosciences and engineering 2024年第12期21卷 7830-7853页

作者： Nisha, M. Kannan, T. Sivasankari, K. Department of Computer Science and Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India Department of Mechanical Engineering Amrita College of Engineering and Technology Tamil Nadu Nagercoil India Department of Electronics and Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain’s medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span. © 2024 the Author(s),

关键词： Convolutional neural networks

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