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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Babu, Tina Nair, Rekha R Kishore, S. Mahapatra, Sheila Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD. © 2025 The Author(s).

关键词： Random forests

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Early Retinal Degeneration Detection with Deep Faster CNNs

Early Retinal Degeneration Detection with Deep Faster CNNs

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International Conference on Advances in Information Technology (ICAIT)

作者： K. Priyadharshini Sindhusaranya Balraj JV Rama Kumar Deepak A. Vidhate Thilakavathy. P P. Vanathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar Maharashtra Computer science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Department of Information Technology Adhiyamaan College of Engineering Hosur

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included. This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration; the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large.

关键词： Training Analytical models Technological innovation Accuracy Systematics Coherence Tomography Retina Data models Diseases

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Anomaly Detection in Evolutionary Social Networks leveraging Community Structure 15

Anomaly Detection in Evolutionary Social Networks leveraging...

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15th IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2021

作者： Ghoshal, Arnab Kumar Das, Nabanita Asutosh College Department of Computer Science Kolkata India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781665467223

Social networks are temporally evolving by nature and in general, the evolution takes place gradually with time. But occasionally, the nodes may exhibit anomalous behavior that is to be detected and reported in real time. Usually, the social networks are represented as a temporal stream of weighted graphs, where edge weights stand for frequency or power of actions. Here, for identification of anomalies in evolutionary social networks, we follow an action based approach leveraging the community structure of the network, where it is shown that only the Community Boundary Nodes are enough to detect anomaly in a temporally evolving social network. We introduce a new definition of anomaly score for nodes based on its action history. To the best of our knowledge, this is the first work to exploit the community boundary nodes of an evolutionary social network to identify the graph snapshots containing anomalous events. Experimental studies on real-world as well as synthetic evolutionary networks show that the proposed technique achieves almost 13% improvement in F-Score compared to the state-of-the-art algorithms. Moreover, parallel implementation of the proposed technique results nearly 6 x speedup making it suitable for real time computation of anomaly in evolutionary social networks. © 2021 IEEE.

关键词： Anomaly detection

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Navigating the Edge: UAS Boundary Tracing for Efficient Volcanic Plume Monitoring

Navigating the Edge: UAS Boundary Tracing for Efficient Volc...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： John Ericksen Abir Islam Carter Frost Kevin Aubert G. Matthew Fricke Varsha Dani Rafael Fierro Tobias Fischer Scott Nowicki Jared Saia Melanie Moses Department of Computer Science University of New Mexico Department of Electrical Engineering University of New Mexico Center for Advanced Research Computing University of New Mexico Computer Science Department Rochester Institute of Technology Earth and Planetary Sciences University of New Mexico Santa Fe Institute Santa Fe

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

We present the implementation and validation of Sketch, an algorithm that uses two Unpiloted Aerial Systems (UASs) to trace the boundary of volcanic plumes. Sketchguarantees asymptotically optimal flight distance and turning by maintaining a sandwich invariant where one UAS stays inside the plume boundary (defined by a $\textbf{CO}_{2}$ concentration threshold) and the other UAS stays outside. The UASs adjust their flight paths based on real-time CO 2 measurements to maintain this invariant. This paper details the implementation of Sketchon a real-world UAS platform, the Dragonfly drone. We evaluate the efficacy of Sketchthrough extensive testing in physics-based simulations and real-world outdoor environments using virtual plumes. The algorithm is compared to a single-UAS baseline algorithm called Zigzag. Results show that Sketchmeets the expectations set by theory, and it is more efficient than Zigzag, achieving shorter flight paths, less turning, and faster mapping times. While Zigzagexhibits slightly higher accuracy in estimating plume area and boundary, Sketchoffers a more efficient real-time volcanic plume monitoring approach, especially in time-sensitive situations. These results demonstrate the feasibility and efficacy of Sketchfor real-time volcanic plume monitoring, paving the way for accurate $\textbf{CO}_2$ emission estimation in hazardous and challenging environments.

关键词： Accuracy Navigation Volcanic activity Estimation Turning Real-time systems Safety Security Monitoring Testing

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Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial Sensors

Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural...

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Digital Futures and Transformative Technologies (ICoDT2), International Conference on

作者： Hamza Ali Imran Shaida Muhammad Saad Wazir Ataul Aziz Ikram Obaidullah Arshad Department of Computing National University of Sciences & Technology Islamabad Pakistan School of Computing Korea Advanced Institute of Science & Technology Daejeon South Korea School of Engineering National University of Computer & Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798350391862

ISBN: (纸本)9798350391879

The current prevalent approach of the Internet of Health and Medical Things entails proactively preventing disease onset through routine monitoring of individuals’ physical activities, making Human Activity Recognition (HAR) and Behaviour Analysis an important field of study. Sports Activity Recognition (SAcR) is a subset of HAR that focuses on identifying athletic movements. computer Vision, Environmental Sensors, and Wearable Sensors are the three main methods for monitoring such activities. When the merits and limitations of these methods are considered, Wearable Sensors emerge as the most practical option. This study introduces the Mukhtasir-Khail-Net, a model with only 651 parameters that efficiently uses addition layers. When applied to six sports activities, the model achieves an impressive average accuracy of 98.865% using inertial sensor data generated by wearable sensors. For benchmark purposes, the presented model is also evaluated on the WISDM 11 dataset which is a Human Activity Recognition dataset from Fordham University and has achieved an accuracy of 94.24%.

关键词： Accuracy Inertial sensors Internet of Medical Things Signal processing Internet Human activity recognition Wearable sensors Monitoring Sports Diseases

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

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Quantifying the Impact of Speaker and Content Features on ASR Systems Using Unsupervised Distance Metrics

IEEE Sensors Reviews

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IEEE Sensors Reviews 2025年 2卷 170-178页

作者： Sreenija Pavuluri Sagnik De Anil Kumar Gupta Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering Visakhapatnam India Institute of Radio Physics & Electronics University of Calcutta Kolkata India HPC I&E Group Centre for Development of Advanced Computing Pune India

Automatic speech recognition (ASR) models have become increasingly sophisticated, yet the underlying mechanisms driving their translation accuracy remain underexplored. This article explores the comparative influence of speaker characteristics and content similarity on ASR model performance, utilizing unsupervised distance metrics and clustering algorithms to gain deeper insights. By conducting a series of experiments using custom datasets, we aim to understand ASR model performance by examining whether the latent space features correlate more with speaker traits, such as accent, pitch, and speaking style, or with the semantic and syntactic content of the speech. Our findings reveal significant insights into the biases and strengths of current ASR technologies, highlighting the balance between speaker-dependent and content-dependent factors. Understanding these dynamics not only enhances the development of more robust and inclusive ASR systems but also paves the way for innovations in speech technology applications. This research contributes to the broader discourse on improving ASR models to better serve diverse populations and varied linguistic contexts.

关键词： Feature extraction Computational modeling Recording Data models Transformers Measurement Convolution Vectors Context modeling Acoustics

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Artificial Intelligence driven Deep Learning for Competitive Intelligence to enhance Market Analysis and Strategic Positioning 4

Artificial Intelligence driven Deep Learning for Competitive...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Shukla, Sunil Singh, Jagendra Nassa, Vinay Kumar Saba, Masarath Bhatia, Jitesh Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Ggsi University Tecnia Institute of Advanced Studies Dept. of Information Communication Technology Delhi India Hyderabad India Epsilon Department of Client Engagement MA United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

This research provides insight into the successful application of modern deep learning techniques such as Transfer Learning, BERT, and Autoencoders to develop a powerful tool to sustain market analysis and strategic positioning in competitive intelligence. The conducted analysis proved that BERT was the most effective technique, as it produced the highest results in regards of accuracy, precision, recall, and F1 score values in all ten trials. The results of the experiment showed that with accuracy ranging from 90.5% to 92.3%, precision from 91.2% to 93.0%, recall from 89.7% to 91.7%, and F1 score from 90.4% to 92.2%, BERT proved to be the best technique for extracting insights from various textual data sources. In and of itself, Transfer Learning also yielded promising results, and its accuracy ranged from 85.2% to 88.6%, precision from 86.5% to 89.9%, recall from 83.7% to 87.3%, and F1 score from 85.0% to 88.5%. Additionally, given its ability to adapt to particular tasks with quite limited labeled data, Transfer Learning is a valuable tool for market analysis. In this experiment for Autoencoders, it has been shown that they provided a slightly lower performance than BERT and Transfer Learning. The reconstruction error of the deep learning method used in this experiment ranged from 0.018 to 0.025, while accuracy varied from 87.9% to 90.3%. Nevertheless, further optimization can make autoencoders a promising approach for market analysis. Thus, this research indicates the vital importance of leveraging advanced deep learning techniques for gaining a competitive advantage in the present-day business environment. © 2024 IEEE.

关键词： Contrastive Learning

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Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

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Journal of Mobile Multimedia 2022年第2期18卷 325-348页

作者： Vinoth Kumar, V. Karthick Raghunath, K.M. Rajesh, N. Venkatesan, Muthukumaran Joseph, Rose Bindu Thillaiarasu, N. School of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India University of Technology and Applied Science Shinas Oman Department of mathematics School of Applied Sciences REVA University Karnataka Bengaluru India Department of Mathematics Christ Academy Institute for Advanced Studies Bangalore India School of Computing and Information Technology REVA University Bengaluru India

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model. © 2021 River Publishers

关键词： Accuracy Blast Bronzing Convolution neuro-fuzzy Crown rot Disease classification Head blight Paddy

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