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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Pavan, A.C. Somashekara, M.T. Bangalore University Department of Computer Science and Applications Bengaluru India

ISBN: (纸本)9798350390759

In this research, we propose a neat dual-tampering detection approach to further retrieve the forged content of 1080p images. The recovering of actual information is derived from the low-frequency components of the second-order wavelet. The procedure initiates with the segmentation of the image into a non-underlying section. To make better the quality of the recuperate image, a radially symmetrical mapping technique is employed to safeguard the recovery bits. The most notable bit-plane of each segment is utilized to generate the first component authentication key through the MD5 algorithm, while the second component authentication key is produced using SHA1 based on the recovered data. Subsequently, the watermarked image is formed by embedding the recovery bits and the dual authentication keys into the least significant bits of the original image. During the tampering detection phase, the calculated authentication keys are compared against the retrieved keys. Any discrepancies result in the block being classified as tampered. Following this, centrosymmetric mapping is applied to extract the low-frequency coefficients of the second-order wavelet for the purpose of image self-recovery. The inverse wavelet transform is then executed to restore the tampered regions with the recovery data, thereby finalizing the self-recovery process. Our experimental findings indicate that this approach yields enhanced recovery quality and more effectively conceals the embedded information. © 2024 IEEE.

关键词： Restoration

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Exploring Machine Learning Strategies for Intrusion Detection in Wireless Sensor Networks 9

Exploring Machine Learning Strategies for Intrusion Detectio...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Saini, Jyoti Kait, Ramesh Kurukshetra University Department of Computer Science And Applications Kurukshetra India

ISBN: (纸本)9798350394474

This survey provides a detailed examination of the integration of machine learning (ML) techniques in intrusion detection systems (IDS) tailored for wireless sensor networks (WSNs). With the growing importance of WSNs across various applications such as environmental monitoring and the development of smart cities, ensuring their security from potential intrusions is of utmost significance. The paper initiates by elucidating the foundational concepts underpinning WSNs and the associated security challenges. This is succeeded by discussing the diverse machine learning paradigms and their pertinence in the WSN domain. The core of this survey is dedicated to an in-depth analysis of the various ML methodologies utilized in intrusion detection for WSNs - covering supervised, unsupervised, and semi-supervised techniques. A meticulous comparative study highlights each method's strengths, limitations, and specific use cases. Furthermore, the paper brings to light the challenges faced when integrating ML within WSNs, with a particular emphasis on issues related to privacy. On a prospective note, the research navigates into the future trends in the realm of WSN security. Here, the promise of enhanced ML algorithms, the potential of hybrid models, and the impact of upcoming technological advancements on the synergy between WSNs and ML are explored. The insights from this review not only validate the game-changing role of ML in bolstering WSN security but also pave the way for further inquiries and innovations in the domain. © 2024 IEEE.

关键词： Wireless sensor networks

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Lane Detection for Autonomous Vehicles with Canny Edge Detection and General Filter Convolutional Neural Network 18

Lane Detection for Autonomous Vehicles with Canny Edge Detec...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kishor, S. Nair, Rekha R. Babu, Tina Sindhu, S. Vishnu Vilashini, S. Department of Computer Applications Mangalore India Alliance University Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India

ISBN: (纸本)9789380544519

Lane detection has been a complex issue that has garnered the attention of the computer vision community for many years. It is a crucial element for self-driving cars and computer vision in general. Lane detection is used to define the path for autonomous vehicles and prevent the risk of drifting into another lane. So proposed a study to develop a method that can detect lane lines in real-time using the OpenCV library and computer vision concepts. To accomplish this, identify the white markings on both sides of the lane. The proposed work focuses on identifying the road lane lines that autonomous cars must adhere to, ensuring that they do not cross into other lanes or drive in the opposite direction, which could lead to accidents. The performance of the work assessed using actual road images and videos captured by the car's front-mounted camera. So proposing a basic and easy algorithm for tacking and detecting lanes. The lanes have been detected through all competitions based on data from the camera, which is carried by algorithms. The proposed work implementing image processing techniques to capture the exact and accurate lane ways and hough transformation techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Arduino UNO Canny Detection DHT11 Hough Transformation LDR sensor

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Fake News Detection Using SRTD Algorithm 4th

Fake News Detection Using SRTD Algorithm

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Mahek Tyagi, Sanjay Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9789819706990

False news detection has become a more important research area as fake news can be harmful to your mental health and makes it harder for people to see the truth. The purpose of this research is to shed light on the issue from the standpoint of foundational linguistic management to provide the groundwork for the detection of misdirection in the media. The fundamental challenge in this field of study is the lack of high-quality data, since it may include both bogus and real reports on a representative sample of the population. In addition to the previously used malleable and dominant truth disclosure structure, a truth acknowledgment approach based on the concept of nearby words has been discussed. The use of almost identical terminology and phrases may help expose controlled fake news. The Jaccard estimate is utilized in the primary computation to determine the rational number of fake news stories with a firm quality score, using the same key phrases. Scores for freedom, mien, and weakness are averaged to arrive at a measure of stability. The SRTD programs are more accurate than other truth identification techniques. The wordnet dictionary can enhance the accuracy when applied with SRTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data mining

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Emerging Trends and Perspectives on Challenges and Opportunities in Cloud Computing: A Systematic Literature Review 5th

Emerging Trends and Perspectives on Challenges and Opportuni...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Tanwar, Jaswinder Tyagi, Sanjay Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9789819716814

The way organizations run, store and process data and provide services to clients has all been changed by cloud computing. Based on the material that has already been published, this systematic review seeks to identify the main potential and difficulties in cloud computing. The research revealed a number of drawbacks with cloud computing, such as security and privacy concerns, a lack of standards, difficulties with interoperability and performance issues. Security concerns have been identified as the most significant challenge, as cloud computing involves sharing data and applications over the internet, which can increase the risk of cyber-attacks and data compromises. Privacy issues have been also identified as a significant challenge, as when data is kept in cloud storage, people might not have control over it. Lack of standards and interoperability challenges are identified as barriers to cloud adoption, as it can be challenging to integrate cloud services with existing IT infrastructure. Performance issues are also a challenge, as cloud services may not always meet performance expectations, leading to user dissatisfaction. The review also identified several opportunities in cloud computing, including scalability, cost savings, flexibility and innovation. Scalability is a significant opportunity, due to the fact that cloud computing enables enterprises to scale up or down their IT infrastructure and resources based on demand. Cost savings are also a significant opportunity;as cloud services compared to conventional IT infrastructure, it may be more cost-effective. Flexibility and innovation are also significant opportunities, as cloud computing enables companies to swiftly introduce new services and applications, allowing them to innovate and stay competitive. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data privacy

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Machine Learning and Artificial Intelligence in Cybersecurity: Innovations and Challenges 2

Machine Learning and Artificial Intelligence in Cybersecurit...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Khatri, Vinayak Agarwal, Gaurav Gupta, Amit Kumar Sanghi, Akash Invertis University Department of Computer Science and Engineering Bareilly India KIET Group of Institutions Department of Computer Applications Ghaziabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Rapid developments in machine learning (ML) and artificial intelligence (AI) have had a big impact on cyber security by providing creative answers to challenging security problems. This analysis examines how AI and ML are integrated into cyber security, emphasizing the uses, advantages, and drawbacks of each technology. To improve threat detection, malware categorization, phishing prevention, and network security, this study analyses many AI and ML methods, such as reinforcement learning, deep learning, supervised learning, and unsupervised learning. According to recent study findings, AI-driven intrusion detection systems can detect intrusions with up to 99% accuracy, which greatly lowers the number of false positives. Comparatively, compared to conventional techniques, machine learning algorithms for malware detection have increased detection rates by 20-30%. However, several technological obstacles stand in the way of AI's smooth integration with cyber security. These obstacles include problems with data quality, adversarial assaults, interpretability of models, and the computing demands of implementing AI at scale. There are other major obstacles, such as privacy and ethical considerations. Nonetheless, issues including adversarial attacks, ethical dilemmas, and poor data quality persist. This paper illustrates the usefulness of AI and ML technologies in cyber security through real-world case studies. Lastly, it emphasizes how AI and ML have the ability to revolutionize cyber security procedures by highlighting upcoming trends and research paths. This review attempts to provide useful insights for researchers, practitioners, and policymakers to harness AI and ML for a safer digital environment by providing a thorough overview and highlighting important discoveries. © 2024 IEEE.

关键词： Phishing

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Physiological and Inertial Features based Dataset for Falls and Activities: PIF v2 2

Physiological and Inertial Features based Dataset for Falls ...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kaur, Rajbinder Sharma, Rohini Dhaliwal, Manpreet Kaur Department of Computer Science and Applications Panjab University Chandigarh India

Automatic detection and prediction of falls specifically in elderly population who live alone can prevent untoward incidents by taking action at appropriate time. Validating these automated systems requires a labeled dataset. The available datasets are mostly based on inertial features. A health-related concern may also be a reason behind a fall. Considering physiological features along with inertial features can increase the accuracy of the system. In this paper, a labeled multiclass dataset is presented based on physiological and Inertial features of the body (PIF v2). Two trials are performed by each participant. The participants are young as well as elderly performing activities and falls in real-time using the prototype containing various sensors designed for collecting inertial as well as physiological features. Two trials are performed by each participant. 27 subjects (15 males and 12 females) between the age 10 years to 75 years participated in data collection. A detailed comparison between PIF v1 where the data were collected individually using each sensor and PIF v2 is given along with statistical analysis of PIF v2. The statistical analysis of various features with a t-test shows that the different activities and falls can be differentiated using inertial as well as physiological features. © 2024 Elsevier B.V.. All rights reserved.

关键词： Statistical tests

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Smart Wearable Devices for Prediction Using Machine Learning Techniques: Depth Exploration 7

Smart Wearable Devices for Prediction Using Machine Learning...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Mary, L. William Antony Raj, S. Albert SRM Institute of Science and Technology Department of Computer Applications India

ISBN: (纸本)9798350372816

The emergence of the COVID-19 epidemic has emphasized the continuous need for innovative and discreet methods to monitor and evaluate the progression of the disease. Wearable technology, equipped with an array of sensors and powered by advanced machine learning algorithms, presents a highly promising solution. These devices make use of sensors like heart rate monitors, pulse oximeters, temperature sensors, and accelerometers to continuously gather real-time data. This research explores how wearables, including fitness trackers and smartwatches, can be used to continuously monitor the COVID-19 disease and capture the data. It also explores applications in assessing COVID-19 severity through the utilization of machine learning techniques. Machine learning techniques including decision trees, support vector machines, and deep neural networks are used to process this data. This includes the early detection of warning signs, evaluating disease progression, and distinguishing between mild, moderate, and severe cases. The integration of patient-reported symptoms with wearable data further enhances the precision of assessing COVID-19 severity. © 2024 IEEE.

关键词： COVID-19

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Emotions: Pyramidal Structured Feature Representation from EEG Signals

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SN computer science 2025年第4期6卷 1-18页

作者： Thejaswini, M.S. Kumar, G. Hemantha Aradhya, V. N. Manjunath Department of Studies in Computer Science University of Mysore Karnataka Mysore 570006 India Department of Computer Applications JSS Science and Technology University Karnataka Mysuru 570006 India

EEG-based emotion recognition offers significant advantages for human-robot interaction, computer gaming, and mental health monitoring. However, current methods face challenges like high-dimensional data, computational complexity, and unrealistic setups. To address these issues, this research proposes a pyramidal structured feature extraction approach utilizing hierarchical forward interpolation and multirate sampling frequencies. A culturally relevant EEG dataset, featuring Kannada musical clips and commercial advertisements, was created for ecological validity. Data were recorded from 46 participants using a BIOPAC 2-channel EEG device, targeting emotional states such as enjoyment, relaxation, sadness, fear, and being scared. The pyramidal approach reduces dimensionality by scaling EEG features from thousands to a compact set while preserving critical information. Iterative forward difference operations are applied across five hierarchical levels, and multirate sampling enhances temporal resolution for better emotion discrimination. The second stage uses a probabilistic neural network, achieving classification rates of up to 98%, outperforming principal and independent component analysis techniques. This framework is computationally efficient, supports real-time applications, and works flexibly with diverse datasets. By integrating culturally relevant stimuli, it sets a new benchmark for EEG-based emotion recognition. Practical applications include emotion-driven product recommendations, entertainment systems, and advancements in human–computer interaction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： EEG Signals Emotional Intelligence Entertainment Numerical Analysis PNN

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Anomalous Network Packet Detection: A Review of Attacks, Datasets and Techniques 2nd

Anomalous Network Packet Detection: A Review of Attacks, Da...

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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Kohli, Mudita Chhabra, Indu Department of Computer Science and Applications Panjab University Chandigarh India

ISBN: (纸本)9789819736003

Anomalous network packet detection is a dire security concern today. ChatGPT era has emerged as a grave concern for computer scientists and programmers. The method of recognizing diverged network activity from the typical behaviour of network flows is known as anomalous network packet detection. As networks have expanded in terms of complexity and size, detecting outlier occurrences in such huge and complex networks has become crucial in retaining integrity. Detecting threats quickly and accurately determines the success of any network flow. To ensure reliable behaviour and capacity to model features, deep learning models have emerged as promising tools in recent years. Developing an efficient anomaly detection system necessitates understanding the imbalanced datasets, recognizing changing patterns and identifying irregularities. The paper outlines attacks, knowledgeable parameters, techniques and datasets for studying crucial network anomalies by reviewing research for different periods of analysis—before 2000, 2000–2010, 2011–2015, 2016–2018 and 2019–2023. The challenges are addressed, and futuristic options for anomaly detection systems are proposed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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