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Ant Colony Optimization based Edge Detection in Digital Images 5

Ant Colony Optimization based Edge Detection in Digital Imag...

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5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Srivastava, Atul Singh, Rajeev Juneja, Shilpa Verma, Garima Amity University Amity School of Engineering and Technology Lucknow India ABES Engineering College Department of Computer Science and Engineering Gaziabad India Nirankari Baba Gurbachan Singh Memorial College Department of Computer Science and Engineering Sohna India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

Edge detection is identifying the discontinuities in the intensity of the pixel and grouping the contour of edges. Edge detection in digital images is a significant phenomenon in computer vision. In the initial stages of development of edge detection techniques, typical methods like Sobel Canny came into existence. These methods are fast but sensitive towards noise. Soft computing techniques like ant colony optimization (ACO), Genetic Algorithms (GA), Particle Swarm optimization (PSO) and Fuzzy Logic System (FLS) have extensive application in image edge detection due to their adaptiveness. In this paper, prominent ACO based techniques for edge detection are discussed and compared. The quality of edges identified by ACO based edge detection technique depends on the choice of constants, pheromone evaporation rate, number of iterations etc. © 2022 IEEE.

关键词： computer vision

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Machine Learning-Based Counterfeit Currency Detection

Machine Learning-Based Counterfeit Currency Detection

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Srishti Srivastava Anshika Panwar Jalaj Bhati Dolly Sharma Amity School of Engineering and Technology (ASET) Amity University Noida India Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Counterfeit currency detection is a critical issue posing significant challenges to economies worldwide. Traditional methods relying on manual inspection are prone to error and inefficiency. To enhance detection accuracy, this study explores advanced machine learning techniques, specifically Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models. Existing SVM models struggle with large-scale datasets, and CNN models require extensive training data. There is also a lack of diversity in counterfeit samples and limited use of augmented data in existing datasets. This research compares SVM and CNN models in identifying counterfeit ₹500 Indian banknotes. Using a dataset of genuine and counterfeit notes, features were extracted with a Histogram of Oriented Gradients (HOG) for SVM. ResNet50 architecture was used for CNN. The dataset was augmented to improve robustness. The CNN model achieved training and validation accuracy, while the SVM model also reached high accuracy. These results highlight the potential of both models in providing accurate and automated counterfeit detection, demonstrating their effectiveness and reliability in this application.

关键词： Support vector machines Training Measurement Accuracy Biological system modeling Feature extraction Robustness Convolutional neural networks Ensemble learning Currencies

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Investigating Blockchain Security Mechanisms for Tamper-Proof Data Storage 2

Investigating Blockchain Security Mechanisms for Tamper-Proo...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thilakavathy, P. Jayachitra, S. Aeron, Anurag Kumar, Narendra Ali, Syed Sumera Malathy, M. Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India School of Computing Dit University Dehradun Computer Science and Engineering India Amity University Department of Computer Science and Engineering Jharkhand Ranchi India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350369960

Across a wide range of sectors, blockchain technology has emerged as a game-changing option for secure data storage. This research dives into the mechanisms that enable blockchain to protect data integrity and immutability. This research investigates how blockchain technology reduces the chances of data tampering and unauthorized adjustments by examining its underlying concepts and features, such as decentralization, cryptography, consensus procedures, and immutability. Blockchain networks are decentralized, so they don't rely on any one entity to function. This eliminates one potential point of failure or manipulation. Data is protected from tampering and disclosure thanks to the use of hash functions and digital signatures, two examples of strong cryptographic algorithms. By confirming and agreeing on transactions, consensus methods like Proof of Work (PoW) and Proof of Stake (PoS) keep the blockchain ledger up-to-date and accurate. By linking blocks together and recording timestamps, blockchain makes it practically impossible to alter data after it has been recorded. The distributed ledger's availability to the public and ease of use encourage accountability and deter dishonest participants. Smart contracts eliminate the need for third parties by automating operations based on predetermined rules that cannot be altered after they have been created. This study also delves into blockchain fork resistance, incentives for truthful involvement, and other network security measures. It also covers the security implications of both permissioned and permissionless blockchains. Blockchain technology provides strong security procedures, but it has several drawbacks that must be taken into account. These include issues with scalability and the possibility of security flaws in smart contracts. Therefore, securing security and performance requires cautiously considering particular use case needs and picking the right blockchain platform and configuration. For blockchain networ

关键词： Blockchain Consensus Algorithms Cryptography Immutable Ledger

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Edge Intelligence Hybrid Framework for Real-Time IoT Applications using Multi-Stage Adaptive Processing Methods 6

Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Suneel, Sajja Yadav, Rakesh Kumar Nirmala Devi, K. Teja sree, B. Aarthi, R. Upadhyay, Shrikant Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Amity School of Engineering and Technology Amity University Bihar Patna India Department of Computer Applications Madanapalle Institute of Technology & Science India Department of IT S.R.K.R Engineering College Bhimavaram India Department of ECE Rajalakshmi Institute of Technology Kuthambakkam Post Tamil Nadu Chembarambakkam600124 India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. This paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. The framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4%, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9–13ms, compared to fog (23–29ms) and cloud (117–125ms), ensuring suitability for time-critical IoT tasks. The proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance. © 2025 IEEE.

关键词： Kalman filters

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Performance Analysis of DGA-Driven Botnets using Artificial Neural networks 10

Performance Analysis of DGA-Driven Botnets using Artificial ...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Manikandan, N. Ruby, D. Murali, S. Sharma, Vandana School of Computer Science and Engineering Vellore Institute of Technology Vellore India Amity Institute of Information Technology Amity University Noida Campus India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

A botnet is a network of devices infected with malware and controlled remotely by a person with malicious intent. Botnets can launch attacks to steal data, perform phishing, spamming and set up DDoS. Some botnets use an algorithm known as DGA- a Domain Generation Algorithm is to evade detection with some well-known blacklist detection methods. Periodically generating a list of candidate control and command server domains. The bot connects with this server with the help of querying DNS servers for the available list of domains one after another till it connects to an existing and relevant server. Creating a DGA based botnets extremely difficult to detect with their high survivability. This paper aims to analyze the Domain Generation Algorithm and implement methodologies to detect DGA-based botnets using Deep Learning and Machine Learning approaches and then compare and contrast these novel techniques with traditional Machine Learning Classifiers. © 2022 IEEE.

关键词： Neural networks

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Android Malware Detection and its Security 6

Android Malware Detection and its Security

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kushwaha, Pradeep Kumar Kumar, Vikas Saraswat, Manish Singh, Pankaj Kumar, Rajesh Vashisht, Swati Amity School of Engineering and Technology Department of Computer Science Amity University Uttar Pradesh Greater Noida India CSE Amity School of Engineering and Technology Amity University Bachelor of Technology Uttar Pradesh Greater Noida India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India Lloyd Law College Uttar Pradesh Greater Noida India ACSE GL Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

The popularity of mobile apps is growing thanks to advancements in technology. This article specifically discusses the Android mobile platform and its layered approach to app development, as well as the security concerns associated with it. Android is an open-source operating system for mobile phones that includes middleware, a user interface, and app software. However, the Android Market's lack of strict security checks means that it is vulnerable to security threats. To counter this, the article proposes a layered approach for developing secure Android apps and the use of an Android Application Sandbox that can identify potentially harmful programs through static and dynamic analysis. © 2023 IEEE.

关键词： Algorithms Android Benign Malware Mobile phones Operating System

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Recipe Recommender System Using BERTopic Modelling Technique 23

Recipe Recommender System Using BERTopic Modelling Technique

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Janmejay Singh Bramah Hazela Pallavi Asthana Shikha Singh Anil Kumar Tiwari Computer Science & Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh India India Electronics and Electrical Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh India India

ISBN: (纸本)9798400709418

Human civilization has been significantly impacted by technology, ushering in an era of tremendous growth. From the dawn of agriculture to the digital era, technical advancements have transformed communication, transportation, healthcare, and other fields. These advancements have not only improved the quality of life, but have also created new ethical and environmental issues, emphasising the intricate interplay between technology and society in our continual journey of development and adaptation. mRecipe Recommender Systems is based on user preferences to provide personalised culinary recommendations. Individual tastes, dietary limitations, and accessible ingredients are analysed by these algorithms to develop personalised meals. The use of such technologies not only improves convenience, but also encourages innovation in the kitchen, encouraging healthier, more sustainable, and pleasant cooking experiences. Interchanges and variations in the recipes are increasing because of ethnic variety, nutritional choices, and health requirements. Globalization and health-conscious dietary choices have necessitated recipes that accommodate a wide range of tastes, cultures, and dietary constraints. The internet era has simplified the access and sharing of varied culinary alternatives, expanding global culinary history. This study's Recipe Recommender uses data preprocessing, feature engineering, and collaborative filtering techniques to use a collection of various recipes and user interactions. This method allows the system to offer meals based on the user's preferences and dietary constraints, boosting the user's culinary experience. This study demonstrates how combining these technologies may revolutionise the art of cooking by providing users with a more pleasurable, personalised, and data-driven culinary experience.

关键词： Data Analytics

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Sentiment Analysis in Social Media Using Deep Learning Techniques

Sentiment Analysis in Social Media Using Deep Learning Techn...

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

作者： Babu, M. Shinde, Pravin Vishnu Agrawal, Neha Bhaskar, M. Vijaya Hemabala, K. Priya, Sweta Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai600124 India Shah and Anchor Kutchhi Engineering College Affiliated to University of Mumbai Department of Artificial Intelligence & Data Science Maharashtra India Maharaja Agrasen Institute of Technology Department of It New Delhi110085 India SriVenkateswara Institute of Technology Department of Computer Science & Engineering Andhra Pradesh Anantapur India Department of Freshmen Tiruvathi517502 India Amity University Department of Amity School of Communication University Patna801503 India

ISBN: (纸本)9798331543617

This work offers a thorough investigation of sentiment analysis in social media using deep learning techniques. Understanding stoner sentiment has become essential for colourful operations like marketing, public opinion research, and recommendation algorithms due to the rapid expansion of social media networks. Deep learning approaches have emerged as crucial tools for sentiment analysis tasks because of their capacity to extract intricate patterns from massive datasets. The usefulness of deep learning models, such as convolutional neural networks (RNNs), and intermittent neural networks (RNNs), and their variations, in extracting complex sentiment information from social media textbooks is investigated in this study. Additionally, the study looks at model infrastructures, point representations, and pre-processing techniques to improve sentiment analysis performance. The results show how effective deep learning techniques are in extracting sentiment directly from various social media posts, opening the door for more sophisticated sentiment-apprehensive digital processes © 2024 IEEE.

关键词： Adversarial machine learning

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A Machine Learning Approach Towards Prediction of User’s Responsiveness to Notifications with Best Device Identification for Notification Delivery

A Machine Learning Approach Towards Prediction of User’s R...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Srivastav, Devansh Srivastava, Hinal Mittal, Naman Deepak Singh, Bayare Bachu, Induvadan Kumar Yadav, Ashwani Gothwal, Pushpa Sharma, Mssk Krishnamurthy, Bhargav Saarbrücken Germany Saarland University Saarbrücken Germany University of Sheffield Sheffield United Kingdom Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India Amity School of Engineering and Technology Amity University Rajasthan Jaipur India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Samsung Research Institute Karnataka Bangalore India University of Southern California Los Angeles United States

ISBN: (纸本)9789819959730

With an increasing number of connected devices, the number of notifications a user receives has multiplied manifolds. With the advent of smart home appliances, this number has even increased. Such a large number of notifications often disturbs the user. Moreover, it remains uncertain to which device among all the connected devices, the notification should be delivered based on the user’s proximity. As a consequence of this, a user might miss some important notifications. In this paper, a system has been proposed that identifies the best device to deliver notifications and uses machine learning to predict user’s responsiveness to notifications. Using random forest classification, the model was able to achieve a mean accuracy of 98.16% with a precision of 93.42% over stratified 10-folds cross-validation for prediction of user’s responsiveness. Based on this responsiveness, the system can decide when to deliver a notification to reduce the user’s notification annoyance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Machine learning

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Extensive Analysis of Deep Learning Algorithms for Automatic Detection of Liver Anomalies Using MRI and CT Scanned Images

Extensive Analysis of Deep Learning Algorithms for Automatic...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Sugandha Chakraverti Smita Sharma Vijay Shukla Department of Computer Science and Engineering Amity School of Engineering and Technology Greater Noida Campus Amity University Uttar Pradesh Amity School of Engineering and Technology Greater Noida Campus Amity University Uttar Pradesh Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The liver's segmentation during medical imaging is crucial for the diagnosis and prognosis of liver diseases., including fibroid cysts, malignant growths, and other pathological problems. The World Health Organization says that alcohol addiction, non-alcoholic fatty liver disease, chronic hepatitis infections, and genetic predispositions are among the risk factors that make liver cancer a primary cause of cancer-related death. Radiologists' traditional manual assessment of CT and MRI images takes a lot of time and is prone to mistakes. Medical picture segmentation has undergone a revolution because of developments in deep learning, namely convolutional neural networks (CNNs)., which automate feature extraction and increase accuracy. This comprehensive analysis investigates many deep learning techniques, such as CNNs, U-Net, autoencoders, GANs, and transformers, that are used for liver segmentation. The above-mentioned models have revealed a significant improvement in efficiency and accuracy in segmentation. These models” evaluation and assessment have been eased with datasets such as LiTS, CHAOS, MSD, SLIVER07, and many others. Some of the performance indices that are used to measure the effectiveness of these mathematical models. Despite this, there are still problems that persist such as the processing requirements, morphological variation, and dependency on labeled data. To improve model clinical relevance in the future, more work should be done to make them less sensitive to noise and to increase model interpretability. This analysis raises questions for further development to overcome the current limitations and reveal the potential of deep learning to transform the identification of liver abnormalities.

关键词： Deep learning Image segmentation Accuracy Liver diseases Computed tomography Magnetic resonance imaging Transforms Transformers Mathematical models Prognostics and health management

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