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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus India Information Technology Indian Institute of Information Technology Prayagraj Allahabad India

ISBN: (纸本)9789819942831

Since the high demand for Human–computer Interaction (HCI), developing an automated model for recognizing facial gestures or emotions becomes challenging. Some experts have used facial images as a constructive part of recognizing different emotions of humans. Simultaneously, emotions can also be classified by using some electrical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), and so on. Among all these signals, EEG captures the major information of brain activities. Recently, along with face images, EEG signal plays a pivotal role in emotion recognition. Though these methods have been estimated the promising results, the real-world implications and diverse feature identification of human states are still in place to gain more attention. Considering these factors, this survey aims to review the literature for analyzing emotion recognition performance using EEG signals and facial images. It also demonstrates the techniques that are utilized in machine learning and deep learning as well. The drawback of such implemented models is discussed, leading to future development. Subsequently, the survey part is followed by exploring the chronological review of the emotion recognition work, dataset utilization, methodologies employed, and experimental analysis with divergent metrics and features, and challenges. Finally, the research challenging factors provoke to raise the novel effective system for emotion recognition using facial images and EEG signals. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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IPDD: Intelligent Parkinson Disease Detection System 4th

IPDD: Intelligent Parkinson Disease Detection System

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Singhal, Manish Tiwari, Vinita Sharma, Ashish Electronic and Communication Indian Institute of Information Technology Rajasthan Kota325003 India Computer Science and Engineering Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819751990

Worldwide, millions of peoples are affected by complex neuro degenerative disorder, that is known as PD (Parkinson Disease). In recent years, it is very critical for accurate diagnosis of PD at early stage for implementing timely interventions and improving patient life. There have been growing interest in developing intelligent systems for Parkinson’s disease detection, harnessing the potential of cutting-edge technologies such as artificial intelligence, machine l earning, and sensor-based data analysis. This research paper presents an in-depth exploration of an Intelligent Parkinson Disease Detection System (IPDDS) designed for early diagnosis and quality level monitoring of PD using two most accurate machine learning techniques—SVM and Random Forest. The presented work relates the use of various performance metrices like Accuracy, Confusion Matrix, Precision, etc. Standard Dataset having voice samples of human’s having Parkinson Disease and not having PD is used to validate the various performance parameters and predict the PD or non-PD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

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A CNN Model Based Approach for Disease Detection in Mango Plant Leaves 12th

A CNN Model Based Approach for Disease Detection in Mango Pl...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Singh, Pankaj Pratap Kumar, Devanshu Srivastava, Aakriti Basumatary, Madhusmita Prasad, Shitala Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Assam India School of Mathematics and Computer Science Indian Institute of Technology Goa Goa India

ISBN: (纸本)9789819732913

In this study, we propose a machine learning (ML) based method for the early detection of plant leaf diseases. Plant diseases are a major concern in agriculture, impacting crop yield, and food security. Early and accurate identification of these diseases is vital for effective disease management. Our approach employs deep learning algorithms and a dataset of annotated images containing both healthy and diseased plant leaves. Through training a deep neural network, we enable automated disease detection by extracting relevant features from leaf images and classifying them as healthy or diseased. By reducing reliance on human expertise, our approach enables timely detection, facilitating prompt implementation of disease management strategies. This ML-based method has the potential to revolutionize the field of plant pathology, offering valuable insights for the development of precision agriculture techniques aimed at ensuring sustainable crop production. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Advanced NID-VGG16 with Orca Predation Optimization Based 1DCNN-BiLSTM for Network Intrusion Detection 14

Advanced NID-VGG16 with Orca Predation Optimization Based 1D...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Ratna Kumari, K. Durga, N. Gayathri, T. Sundari, Ch. Lalitha Syama Uppalapati, Padma Jyothi Shri Vishnu Engineering College for Women Computer Science and Engineering Bhimavaram India Sasi Institute of Technology and Engineering Information Technology Tadepalligudem India Vishnu Institute of Technology Computer Science and Engineering Bhimavaram India

ISBN: (纸本)9798350344837

Many cybersecurity dangers exist for modern computer networks, which emphasize how crucial Network Intrusion Detection (NID) is necessary. The ever-changing threat landscape has made it difficult for traditional intrusion detection systems (IDS) to remain successful, which emphasizes the growing significance of artificial intelligence (AI) solutions. To improve threat detection effectiveness, the paper used Deep Learning-powered network intrusion detection (DL-powered NID) in this study. The tests utilized two datasets: the Canadian institute for Cybersecurity 2017 (CICIDS2017) dataset and Internet of Things network traffic (IOT23) captures to verify the efficacy of this technique. Preprocessing the dataset entails applying the Preprocessing and Minimax Scaling (PMS) method, which includes filtering, transforming, and normalizing the data. The paper offer the NID-VGGI6 framework, a 16-convolution-layer network intrusion detection visual geometry group based on Convolutional Neural Networks (CNNs), for feature extraction. In order to provide a class and scale-invariant architecture, this framework combines multilevel and multiscale features with data augmentation approaches. During NID-VGGI6 training, the focal loss function solves class imbalance, and the Flatten-T Swish (FTS) activation function reduces gradient vanishing and explosion problems. In order to improve decision-making in NID, a one-dimensional CNN-based Bidirectional Long Short-Term Memory (1DCNN-BiLSTM) model is used for classification after feature extraction. The paper utilized the Orca Prediction Optimization Algorithm (OPOA) to fine-tune classification hyperparameters for maximum accuracy. The findings show that the suggested model performs better than the existing ones and reaches an impressive 99.9% accuracy rate for both the datasets. © 2024 IEEE.

关键词： Feature extraction

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Empirical techniques for effort estimation in designing effective ML models

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International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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Trajectory Planning and Data Collection of UAVs Over Disaster a Affected Areas 10th

Trajectory Planning and Data Collection of UAVs Over Disas...

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Dahal, Abisek Ghosh, Santanu Kuila, Pratyay Biswas, Tarun Department of Computer Science and Engineering National Institute of Technology Sikkim737139 India Indian Institute of Information Technology Ranchi Ranchi India

ISBN: (纸本)9789811975233

With the advancement of technology, UAVs have been widely used for civilian and commercial purposes like, military field, disaster areas, firming, etc. Proper planning of the UAVs trajectory is very important for proper and efficient utilization of such devices. Path planning is considered one of the critical tasks during data collection from devices deployed on the ground. In this paper, genetic algorithm (GA)-based energy efficient path planning algorithm is proposed for post-disaster scenario. Efficient chromosomes are designed for the problem. Fitness function is designed for the proposed GA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

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SN computer science 2024年第1期5卷 1-11页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

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A manipulator control method based on deep deterministic policy gradient with parameter noise

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Neural Computing and Applications 2024年第4期37卷 1729-1739页

作者： Zhang, Haifei Xu, Jian Lei, Liting Wu, Fang Qian, Lanmei Qiu, Jianlin School of Computer and Information Engineering Nantong Institute of Technology Yongxing Road 211 Nantong226002 China School of Information Science and Technology Nantong University Seyuan Road 9 Nantong226019 China

Focusing on the motion control problem of two link manipulator, three deep reinforcement learning models named the deep deterministic policy gradient (DDPG), asynchronous advantage actor-critic(A3C) and distributed proximal policy optimization (DPPO) are established for training according to the target setting, state variables and reward & punishment mechanism of the environment model. And then the motion control of two link manipulator is realized. After comparing and analyzing the three models, the traditional DDPG approach based on action noise converges faster and has a higher average reward compared to the other two algorithms. So, DDPG approach based on parameter noise is designed for further research to improve its applicability, so as to cut down the debugging time of the manipulator model and reach the goal smoothly. The experimental results indicate that the DDPG approach based on parameter noise can control the motion of two link manipulator effectively. The convergence speed of the control model is significantly promoted and the stability after convergence is improved. In comparison with the traditional control approach, the DDPG control approach based on parameter noise has higher efficiency and stronger applicability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Utilizing AI-powered Drones for Efficient Flood Rescue Operations 3

Utilizing AI-powered Drones for Efficient Flood Rescue Opera...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kumar, M. Jaswanth Bitra, Dhanush Titus, Rohan Jazz, Dill Kiliroor, Cinu C. Dept. of Electronics and Communication Indian Institute of Information Technology Kottayam Kerala Kottayam India Dept. of Computer Science Indian Institute of Information Technology Kottayam Kerala Kottayam India

ISBN: (纸本)9798331532420

In times of severe earthquakes and floods, rapid and effective aid is crucial. Traditional methods often face challenges due to accessibility issues and the vast scale of *** proposed Autonomous Aerial Humanitarian Assistance and Disaster Relief (A2-HADR) System aims to transform disaster response through the use of drone technology. The proposed solution addresses the obstacles in rescue operations by employing drones equipped with Artificial Intelligence (AI) to provide immediate support. The advanced system utilizes state-of-the-art computer Vision technology to autonomously detect people and obstacles from heights of 50-100 meters at various angles and deliver essential supplies like food, clothing, and rescue gear to those in need. We have executed and assessed a range of advanced object detection algorithms, such as YOLOv8, YOLOv9, and Detectron2. After thorough evaluation, Detectron2 emerged as the most effective model among those tested, showcasing exceptional accuracy and resilience. © 2024 IEEE.

关键词： Drones

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Identification and diagnosis of cervical cancer using a hybrid feature selection approach with the bayesian optimization-based optimized catboost classification algorithm

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3459-3477页

作者： Dhar, Joy Roy, Souvik Computer Science and Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India Department of CSE JLD Engineering and Management College West Bengal Baruipur743610 India

Cervical cancer is the most prevailing woman illness globally. Since cervical cancer is a very preventable illness, early diagnosis exhibits the most adaptive plan to lessen its global responsibility. However, because of infrequent knowledge, shortage of access to pharmaceutical centers, and costly schemes worldwide, most probably in emerging nations, the vulnerable subject populations cannot regularly experience the test. So, we need a clinical screening analysis to diagnose cervical cancer early and support the doctor to heal and evade cervical cancer?s spread in women?s other organs and save several lives. Thus, this paper introduces a novel hybrid approach to solve such problems: a hybrid feature selection approach with the Bayesian optimization-based optimized CatBoost (HFS-OCB) method to diagnose and predict cervical cancer risk. Genetic algorithm and mutual information approaches utilize feature selection methodology in this suggested research and form a hybrid feature selection (HFS) method to generate the most significant features from the input dataset. This paper also utilizes a novel Bayesian optimization-based hyperparameter optimization approach: optimized CatBoost (OCB) method to provide the most optimal hyperparameters for the CatBoost algorithm. The CatBoost algorithm is used to classify cervical cancer risk. There are two real-world, publicly available cervical cancer-based datasets utilized in this suggested research to evaluate and verify the suggested approach?s performance. A 20-fold cross-validation strategy and a renowned performance evaluation metric are utilized to assess the suggested approach?s performance. The outcome implies that the possibility of forming cervical cancer can be efficiently foretold using the suggested HFS-OCB method. Therefore, the suggested approach?s indicated result is compared with the other algorithms and provides the prediction. Such a predicted result shows that the suggested approach is more capable, reliable,

关键词： Genetic algorithms

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