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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Mitra, Poojarini Datta, Suseta Bhattacharjee, Subhasree Kundu, Pritam Dept. of CSE - Data Science Techno Main Salt Lake Kolkata India Dept. of Computer Application Narula Institute of Technology Kolkata India Dept. of AI.Cloud Tata Consultancy Services Ltd. Kolkata India

ISBN: (纸本)9798331512248

Malware has become a major worry as the use of platforms in sensitive applications such as banking has grown. The arduousness of apprehending such malware engenders a range of challenges due to the inhibited resources available and the circumscribed skills supplied to the utilizer, but it also offers up incipient possibilities in the needed information associated with each entreaty. Malware detection predicated on machine learning algorithms is widely used to forfend and bulwark unique data such as personal portal passwords, personal information, and online transactions. Machine learning classifiers are successful at detecting and ceasing malware assaults. This paper describes some machine learning algorithms that are trained to distinguish between legitimate setup files and malware samples. Some error values have been quantified based on malware samples and legitimate setup files, and 99.99% of endeavor precision has been computed, primarily predicated on the discombobulation matrix of a finely geared-up approach. To make this project more user-friendly, a tool has been constructed as an interactive and dynamic web page. In the raw dataset, attributes that distinguish malware domains from regular domains have been used as examples. The malware sample in the body of the message, including the logo and other legitimate content, is designed to appear to be heading for a fraudulent company. © 2025 IEEE.

关键词： a utilizer amicable web framework machine learning approaches natural language processing

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection

arXiv

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arXiv 2022年

作者： Seth, Pratinav Khan, Adil Gupta, Ananya Mishra, Saurabh Kumar Bhandari, Akshat Dept. of Data Science and Computer Applications India Dept. of Mechanical Engineering India Dept. of Electronics and Comm. Engineering India Dept. of Computer Science and Engineering India Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid overconfident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions. Implementation is available at https://***/ptnv-s/UATTA-ENS. © 2022, CC BY.

关键词： Diagnosis

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation

FedERA: Framework for Federated Learning with Diversified Ed...

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Federated Learning Technologies and applications (FLTA), International Conference on

作者： Anupam Borthakur Aditya Kasliwal Asim Manna Dipyan Dewan Debdoot Sheet Centre of Excellence in Artificial Intelligence Indian Institute of Technology Kharagpur West Bengal India Dept. of Data Science and Computer Application Manipal Institute of Technology Manipal Karnataka India Department of Electrical Engineering Indian Institute of Technology Kharagpur West Bengal India

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

Federated Learning (FL) is a distributed machine learning (ML) approach that allows multiple devices to train a model while keeping training data private on edge devices. However, existing FL libraries are incapable of: (i) su pporting a wide range of existing FL algorithms, (ii) handling diverse public as well as custom datasets, (iii) discarding unreliable updates from malicious edge devices, (iv) facilitating edge-device training, and (v) implementing model compression to reduce communication overhead. With the rapid progression in AI, there is an increment in the computation power while training the models on edge devices which results in more carbon emission (CE). However, in most of the existing FL frameworks, there is no module for estimating CE. In this paper, we present FedERA, a modular and fully customizable open-source FL framework aiming to address these issues especially also by offering extensive support on heterogeneous edge devices and by incorporating both standalone and distributed training approaches. The integration of new software modules unforeseen in existing FL frameworks not only amplifies the scope of its usability but also fosters environment-friendly FL. We believe that FedERA will be a valuable tool for researchers and practitioners working in FL.

关键词： Training Federated learning Operating systems Computational modeling Training data Carbon dioxide Real-time systems Software Security Usability

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Block-chain enabled secure smart IoT healthcare monitoring for COVID-19 patients

Block-chain enabled secure smart IoT healthcare monitoring f...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Banerji, Nandan Debnath, Bikash Bandyopadhyay, Krishnan Sikkim Manipal Institute of Technology Dept. of Computer Science and Engg Sikkim India Dayananda Sagar College of Engineering Dept of Medical Electronics Bangalore India

ISBN: (纸本)9781665453424

During these pandemic days, the situation is hazardous to move physically for treatment. Also, the monitoring of health with wearable devices becomes easier with attachment of IoT smart-health facilities. The established hospitals are enabled to provide smart-health care. The wearable devices sense different heath index parameters and sends to the health monitoring agencies including their legal guardians. During such communication, data can be theft or changed, which may increase unwanted risks for the patients. Sometimes, that harms the mental health by passing miss information to patients and their concern guardians too. The proposed approach helps to reduce such chances of data theft with a simple implementation of hyper ledger fabric block chain with the associated devices meant for smart health care monitoring. For the scattered and predominantly adhoc deployment of IoT devices, we use block-chain consortium to secure the communication. From the obtained results, we found that by leveraging block-chain hyper ledger, such issues with the association of limited capacity IoT devices for smart-health care, likely preferable to solve. © 2022 IEEE.

关键词： Internet of things

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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2024 IEEE International Conference on Intelligent Systems and Advanced applications, ICISAA 2024

作者： Seva, Banothu Jain, Ratnesh Khare, Neha Choudhury, Hussain Ahmed Chakravarthi, M Ayyappa Tomer, Nitin MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Design M.P Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P Bhopal462044 India Barak Valley Engineering College Department of CSE Assam Karimganj India KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India GGITS Cyber Security & Blockchain Dept of IOT M.P. Jabalpur India

ISBN: (纸本)9798331539948

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. Security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets. © 2024 IEEE.

关键词： Adversarial machine learning

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UAV Aerial Survey and Communication

UAV Aerial Survey and Communication

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2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2021

作者： Samanth, Snehal Prema, K.V. Balachandra, Mamatha Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Science and Engg. Manipal India

ISBN: (纸本)9781665412445

Over the past several decades, Unmanned Aerial Vehicles (UAVs) have been used in a variety of applications with 2 basic classifications of UAVs' a scivilian and military drones. Drones capture a variety of multimedia data. Among the multimedia data, images with overlapping regions need to be stitched to generate a panorama which would provide image data of 'n' number of images captured by a drone. The data captured by drones should be effectively communicated to a Ground Control Station (GCS). Hence in the research, 4 drones capture both text data and images. Each drone generates a corresponding panorama for the set of images captured by it and communicates both its text data and panorama to the GCS. 2 desktops are used for performing the experiments using client-server communication. Client desktop is used for performing simulations using AirSim simulator (which consists of 4 drones) on the Unreal Engine 4.25 platform, and generate panoramas for the set of images captured by each drone. Server desktop acting as GCS is used to accumulate text data and image data from 4 drones. Image stitching analysis has been done using 2 Python versions and Open CV versions, and 2 AirSim environments. Image stitching results were more effective with the use of Python version 3.7.1 and Open CV version 3.4.2 pair (image stitching success rate, and image stitching accuracy = 100%) when compared to that with Python version 3.9.1 and Open CV version 4.5.2 pair (image stitching success rate = 75%, image stitching accuracy = 33.33%). Both the text data and panoramas from 4 drones were successfully transmitted to the GCS. © 2021 IEEE.

关键词： Drones

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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Prashant Vats Pravin R. Kshirsagar Kamal Upreti Keshav Lalit Tan Kuan Tak Shubham Mahajan Dept. of Computer Science and Engg Manipal University Jaipur Jaipur India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Dept. of Computer Science CHRIST (Deemed to Be University) Delhi NCR India Dept. of Computer Science and Engg Manipal Institute of Technology Manipal Karnataka India Engineering Cluster Singapore Institute of Technology Singapore Amity School of Engineering and Technology (ASET) Amity University Gurugram India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models.

关键词： Deep learning Analytical models Machine learning algorithms Computational modeling Anxiety disorders Mental health Predictive models Depression Physiology Long short term memory

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Identification of the Video Level for Video Communication in a Resource-Constrained Network

Identification of the Video Level for Video Communication in...

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Artificial Intelligence and data Engineering (AIDE), International Conference on

作者： Gururaj Bijur Ramakrishna M Karunakar A. K Department of Computer Science & Engineering Manipal Institute of Technology MAHE Manipal Udupi Karnataka India Department of Data Science & Computer Applications Manipal Institute of Technology MAHE Manipal Udupi Karnataka India

HEVC, which employs layered video coding techniques, is an extension of H.264 Advanced Video Coding. Layered video coding creates a valid bitstream by removing layers. Video streaming via wireless networks is made possible by this capability. The elements that affect the quality of video communication include node mobility, the kind of routing protocols, communication distance, etc. The video adaptation method calculates the number of video levels that will be lost and then extracts at Media Aware Network Elements in accordance with terminal capabilities and resource availability. Building the information needed to select the extraction point or adaption level is the goal of this work. The extraction points for streaming Multi-level Video over the resource constrained network have been calculated in this article.

关键词： Wireless networks Dynamics Bandwidth Streaming media Media Routing Routing protocols

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