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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： B. Hariprasanna S. Vinita Sushila Devi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

Finding boundaries within images is the aim of edge detection, a basic job in computer vision, image processing, and pattern recognition. It can be difficult to detect edges in noisy images or under different situations using traditional edge detection techniques like the Sobel and Canny edge detectors. Motivated by the collective behavior of ants and bees in nature, this work investigates the use of Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) algorithms for edge identification. For difficult tasks like edge detection, these swarm intelligence techniques can efficiently search through vast solution spaces and offer the best answers. The ACO algorithm investigates potential edge sites and optimizes them using pheromone updates by simulating the foraging behavior of ants. However, the BCO algorithm uses the exploration and exploitation behavior of the bees to identify specific boundaries in the image. We compare how well the two systems detect edges in pictures with different noise levels and image quality. The findings demonstrate that edge detection performance is much enhanced by both ACO and BCO, with BCO offering quicker convergence and more precise edge localization. The study shows that ACO and BCO have the potential to be useful tools for detecting edges in noisy and complex images.

关键词： Ant colony optimization computer vision PSNR Image edge detection Image processing Pattern recognition Noise measurement Optimization Noise level Convergence

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An Approach for Word Segmentation from a Line Segment in Odia Text Using Quartiles 5

An Approach for Word Segmentation from a Line Segment in Odi...

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5th International Conference on Computational Intelligence and Networks, CINE 2022

作者： Kar, Aradhana Pradhan, Sateesh Kumar Utkal University Department of Computer Science & Applications Odisha Bhubaneswar India

ISBN: (纸本)9781665464659

This paper deals with word segmentation from a given line segment. These line segments may have alphabets and matras in one single line segment or the alphabets and matras of a line text in two different line segments. The line text is segmented into alphabets and the associated matras in two different line segments are reconstructed using Reconstruct Module. The approach introduced in this paper has three phases: Pre_Processing Module, Find_White_Spaces Module, and Analyse_White_Spaces Module. The Pre_Processing module is responsible for reading the input line segment, converting it to a gray image, removing white spaces that encapsulate the whole text, and then converting it to a binary image. The Find_White_Spaces module is responsible for finding the start and end of the white spaces between the words. The Analyse_White_Spaces module is responsible for analysing the widths of the white spaces using quartiles and storing the segmented words in the directory, 'Segmented Words'. The proposed system has been tested with images of line segments consisting of only alphabets and alphabets with matras. The overall correctness accuracy of 99.9% has been achieved in this approach for word segmentation. © 2022 IEEE.

关键词： Binary images

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Integrating AI into Software Engineering: A Critical Review and Future Directions

Integrating AI into Software Engineering: A Critical Review ...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Aditya Pai, H. Sharath, K.R. Babalad, Kavita Guru Prasad, M.S. Chandrappa, S. Kumar, Pratik Mit School of Computing Mit Art Design and Technology University Pune India Department of Computer Science and Engineering Dehradun India School of Computing and Information Technology Reva University Bangalore India Bengaluru India Department of Computer Applications Dehradun India

ISBN: (纸本)9798331527495

The field of Artificial Intelligence (AI) has been witnessing a huge demand in the field of research, tools development, and applications of deployment. There are multiple software companies which are shifting their focus to developing AI systems, many are also deploying the AI paradigms to their current system. Correspondingly, the academic research community has also used AI paradigms to solve the traditional software engineering problems. AI has become very useful to the software engineering community. The paper also uses a survey of the most commonplace methods of using AI into the Software Engineering, that covers the methods for SE phases such as Requirements, Design, Development, Testing, Release, and Maintenance. The paper also aims in answering to the sufficient intelligence in the SE lifecycle, this evaluates the overlapping of the SE and AI domains, and challenge the current conventional wisdom of the state-of-the-art. © 2025 IEEE.

关键词： Software testing

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Automatic Detection of Rice Diseases using Deep Convolutional Neural Networks with SGD and ADAM 4

Automatic Detection of Rice Diseases using Deep Convolutiona...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Seelwal, Pardeep Sharma, Alok Baba Mastnath University Department of Computer Science and Applications Rohtak India

ISBN: (纸本)9781665474368

Rice diseases are the major problem in all over the world of agriculture sector. The early detection of this disease will prevent the huge economic loss for the farmer. This paper proposes a deep learning algorithm to classify the disease in the rice plant. Images of healthy and blast disease affected leaves are taken for the proposed system. The features are extracted for the healthy and brown spot, leaf blast, hispa disease of the rice leaf. The total data set is divided for training and testing purposes. These images are processed with the proposed multi-classification method and the leaf is categorized into brown spot, leaf blast, hispa disease and healthy. In this work CNN model is employed using two different optimizers i.e. SGD and ADAM. It is found that SGD performed well as compared to ADAM for our application as the training accuracy is obtained from proposed model with ADAM optimizer is 92.04 % while with SGD produced the training accuracy 96.32 %.The proposed model capable of getting promising accuracy for healthy and brown spot, leaf blast, hispa disease of the rice using multi-classification model. © 2022 IEEE.

关键词： Convolutional neural networks

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Predicting Air Quality in India Using Machine Learning Models 4

Predicting Air Quality in India Using Machine Learning Model...

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

作者： Gupta, Swati Kumar, Himanshu Vijarania, Meenu Gautam, Anjali Negi, Neerja Dadhich, Priyanka Kr Mangalam University Department of Computer Science Centre of Excellence Haryana Gurugram India Kr Mangalam University Department of Cse Haryana Gurugram India School of Computer Applications Manav Rachna International Institute of Research & Studies India

ISBN: (纸本)9798350387490

India's poor air quality is responsible for many health concerns, with chronic exposure to fine particulate matter (PM) leading to various health issues. Recently, the pollution levels in India have undergone various changes in the status of pollutants present in the air. Our paper aims to provide a comprehensive analysis of these issues highlighting the severe implications of poor air quality on public health in India and try to predict future health concerns due to poor air quality. In our work we have taken three machine learning modelslinear regression, decision tree regression, and K-Nearest Neighbour into the consideration to train our model. We have evaluated the models by using 'R-squared score' and 'mean square error', to determine which was the best suitable model in predicting the Air quality with better accuracy. Our work contribute to ongoing efforts to improve the air quality forecasts model (AQI) and create effective solutions for a healthier India. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Vision Systems for Rice Diseases Detection: A Review 2

Machine Vision Systems for Rice Diseases Detection: A Review

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Seelwal, Pardeep Sharma, Alok Baba Mastnath University Department of Computer Science and Applications Rohtak India

ISBN: (纸本)9781665437899

To get maximum value added products, quality product monitoring is the most fundamental requirement. Pro-duction of agriculture products can be minimized due to many of the reasons. The fundamental key factor of the quality reduction is the diseases or fungal infection present in the plants. Consequently controlling the diseases or infection in plants require substantially enhancement of the quality production. These infections can lead to severe damage to the rice crop production. Diseases present in rice plants have a critical effect on crop production and accurate detection of disease is the only way to mitigate their effects on the plants. It is a complex task to accurately recognize the disease manually. Early detection and apt remedies is important to expediting the healthy production of the rice plant in order to supply adequately. It also provides foodsecurity to the promptly growing population. The current review addresses the present and projected machine vision approaches for detecting rice disorders. Various techniques have been used to detect the disease present in rice crops is discussed in this paper also associated limitation is also presented. As a result, thispaper discussed the non-destructive and possible applications of machine vision techniques for rice disease diagnosis in depth. © 2022 IEEE.

关键词： computer vision

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Improving Handwritten Stenography Recognition Using Domain Specific Encoding and Synthetic Data Augmentation

Improving Handwritten Stenography Recognition Using Domain S...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Nivetha C. Bala Kamatchi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Handwritten form of stenography recognition is a challenging task due to high variations in shorthand symbols and a lack of annotated datasets. A strong baseline for stenographic text recognition, leveraging the newly created LION dataset of shorthand manuscripts. To alleviate symbol ambiguity as well as low-resource constraints in this domain, incorporating stenographic domain expertise is proposed through encoding schemes and synthetic data generation. In the experiments, a Gated-CNN-BGRU architecture is used, trained with Connectionist Temporal Classification (CTC) loss. Pre-train on synthetic data and fine-tuning on real data greatly improve CER and WER. The experiments showed the effectiveness of stenography-specific encoding schemes in improving WER (Word Error Rate) over baseline models by up to 10% when using Melin-based encodings. This work opens pathways for transcribing historical stenographic manuscripts more effectively.

关键词： Deep learning Handwriting recognition Text recognition Error analysis Symbols computer architecture Logic gates Writing Encoding Synthetic data

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A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

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computer Systems science & Engineering 2023年第8期46卷 2123-2140页

作者： Saurabh Adhikari Tushar Kanti Gangopadhayay Souvik Pal D.Akila Mamoona Humayun Majed Alfayad N.Z.Jhanjhi School of Engineering Swami Vivekananda UniversityIndia Department of Computer Science and Engineering Sister Nivedita University(Techno India Group)KolkataWest BengalIndia Sambalpur University SambalpurIndia Department of Computer Applications Saveetha College of Liberal Arts and SciencesSIMATS Deemed to be UniversityChennaiIndia Department of Information Systems College of Computer and Information SciencesJouf UniversityKSA College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia School of Computer Science(SCS) Taylor’s UniversitySubang Jaya47500Malaysia

Machine learning is a technique for analyzing data that aids the construction of mathematical *** of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction *** type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect *** rise of touch-free applications and the number of deaf people have increased the significance of hand gesture *** applications of hand gesture recognition research span from online gaming to surgical *** location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in *** gestures may be difficult to distinguish from nonsensical motions in the field of gesture *** gestures may be difficult to distinguish from nonsensical motions in the field of gesture *** this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or *** a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered *** have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture *** findings from the simulation support the accuracy,precision,gesture

关键词： Machine learning gesture recognition framework accuracy rate precision rate gesture recognition rate sensitivity rate efficiency rate

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Stacked Variational Autoencoder in the Classification of Cardiac Arrhythmia using ECG Signals with 2D-ECG Images

Stacked Variational Autoencoder in the Classification of Car...

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2022 International Conference on Intelligent Innovations in Engineering and Technology, ICIIET 2022

作者： Nithya, S. Mary Shanthi Rani, M. Gandhigram Rural Institute Department of Computer Science and Applications Dindugal India

ISBN: (数字)9781665456531

ISBN: (纸本)9781665456524

Cardiac Arrhythmia is an endangered signal to human life. Most arrhythmias are shortfalls of symptoms. Electrocardiogram (ECG) is a non-invasive, low-priced, and powerful tool to record the electrical signals of the heart and detect severe cardiovascular diseases. ECG interpretation is generally done by the cardiologist which is time-consuming and sometimes may lead to the wrong diagnosis. Moreover, numerous arrhythmia heartbeats remain unexplored. The arrhythmia record has an expeditious and divergent ECG. Prompt diagnosis of arrhythmia can reduce the mortality rate. In this paper, we have investigated the application of a Stacked Variational Autoencoder (SVAE) for the automatic diagnosis of arrhythmia from ECG signals. Furthermore, the augmented dat aset is used for training the model, to resolve the imbalance in the classes. The proposed model reached an overall accuracy of 98.96% and sensitivity of 97.32%. SVAE classified twelve classes of cardiac arrhythmia including normal sinus rhythm. © 2022 IEEE.

关键词： Electrocardiograms

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Modeling & Verification of an Adaptive IoT System using Uppaal 3

Modeling & Verification of an Adaptive IoT System using Uppa...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Geetha Lekshmy, V. Pillai, Amal S Raj, Arun Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Design time verification of Internet Of Things (IoT) systems helps in detection of errors at an early stage. This study focuses on the modeling and verifying IoT systems that are self-adaptive. Self Adaptive Systems (SAS) adapt autonomously to changing environment and internal dynamics to achieve required functionalities. The adopted case study is the Smart Home Automation System. A self adaptive smart home system is modelled using Uppaal, an integrated tool used for modeling and verification of real-time systems. In Uppaal, systems are modelled using a network of timed automata and verified using statements in Linear Temporal Logic. Two subsystems of smart home, fire detection and automated lighting system are modelled and its properties are verified to ensure correctness of the system. A typical SAS has a feedback loop consisting of MAPE-K (M-Monitor, A-Analyze, P-Plan, E-Execute, K-Knowledge) components, for keeping track of changes in the system, automatically update the knowledge base and execute corrective actions to cope up with the changes in the system. Self adaptive nature of fire detection subsystem in the case study enables the system to automatically switch to inputs from temperature sensors for fire detection in case of failure/ malfunctioning smoke sensor, thus preventing system failure. Automated lighting system changes the light intensity depending on changing activity of a person in a particular area. Modelling and verification of SAS is essential for avoiding run-time failures of safety-critical systems. © 2022 IEEE.

关键词： Internet of things

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