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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Aditya, Harshit Ramani, Anchita Kumar, Nilesh Kasturi Bhattacharjee, Vandana Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Majitar India Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350385199

Standard chest X-rays are used to diagnose pneumonia, an infection of the lungs that can be highly contagious due to being caused by viruses and bacteria. Nevertheless, this method is more time-consuming and routine-dependent with a bias in outcome. So to overcome these problem we developed PneumonXNet, a bespoke CNN model for the detection of pneumonia. We evaluated the performance of our method with respect to pre-trained models such as VGG16, EfficientNetB3 and ResNet50 on a X-ray image dataset comprising 5,856 images. The best result we have reached is a test accuracy of 97% with dropout value of 0.4 which is better than the pretrained models. The pre-trained model EfficientNetB3 showed the highest accuracy, which points on possible applications of transfer learning. This research signifies the capabilities of CNN models to streamline and improve detection on pneumonia, which in turn paves way for technological sophistication in health care sectors. The findings of this study carry significant impact on pneumonia detection, emphasizing the importance of deep learning model in pneumonia detection and how it can help in saving a lot of time for doctors and patients from the traditional X rays collecting systems. This can also reduce the overall treatment cost for patients. This research therefore offers new avenues for developing strong techniques for pneumonia identification, which hold promise for improving healthcare technology and patient outcomes in general. © 2024 IEEE.

关键词： Lung cancer

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Deep learning applications in the Internet of Things: a review, tools, and future directions

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Evolutionary Intelligence 2024年第5-6期17卷 3621-3654页

作者： Raoufi, Parisa Hemmati, Atefeh Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin64002 Taiwan

The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of machine learning (ML), has shown great promise in addressing the challenges of processing and analyzing such data. Considering the increasing importance of DL and data analysis, we decided to review the articles of the last few years in this field to pave the way for researchers. In this article, we used the systematic literature review (SLR) method, and in line with that, we selected and analyzed 56 articles published from 2019 to April 2024. We first discuss the DL models used in the IoT field and clarify their specific use cases. Secondly, we outline an analysis of research areas in DL-based IoT. In addition, our research extends to the tools and simulators used to evaluate studies in the DL-based IoT domain. We also examine the DL-based IoT research datasets. Finally, our review identifies future directions and open issues in DL-based IoT. We aim to contribute to an accurate understanding of the current state, challenges, and potential breakthroughs at the intersection of DL and the IoT. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Internet of things

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Modified light GBM based classification of malicious users in cooperative cognitive radio networks

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Cyber-Physical Systems 2024年第1期10卷 104-122页

作者： Sekar, S. Jeyalakshmi, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Signal to noise ratio

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Local Mean Directional Intensity Pattern: An Efficient Descriptor for Hand Gesture Recognition Using SVM Classification 2nd

Local Mean Directional Intensity Pattern: An Efficient Descr...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Bahuguna, Arti Bhaumik, Gopa Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim India Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819754113

Hand Gesture Recognition (HGR) promotes the most efficient and natural communication between users and machines. However, the performance of the HGR system is deteriorating due to challenging conditions such as illumination variation, complex backgrounds, inter-class similarities. In this paper, a handcrafted feature descriptor named as Local Mean Directional Intensity Patterns (LMDIP) is proposed for effective hand gesture recognition in the aforesaid challenging environment. The proposed LMDIP descriptor aims to maintain the most relevant directional information. It assists in the detection of grey level variations that move in different directions. LMDIP explores differential excitation by calculating the mean intensity of pixels across each angular neighboring pixel. The most significant effect of employing the mutually beneficial connections between adjacent neighbors in different angular directions is that it no longer relies solely on the sign of the intensity differences between the center pixel and one of its neighbors, but also on the sign of the gap across its adjoined neighbors. The performance of the proposed LMDIP is evaluated in terms of accuracy and F1-score on five benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, ASL Static Dataset, NUS Dataset, and OUhand Datasets. The result of the experimental show that the proposed LMDIP achieves an accuracy of 92% (MUGD Set1), 98% (MUGD Set2), 75%(MUGD Set3), 61%(MUGD Set4), 71%(MUGD Set5),99%(ASL Digit), 51%(ASL Static), 83%(NUS-I Dataset), 39% (OUhands Dataset) and respectively which is better compared to the state-of-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gesture recognition

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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A Flow-Based Performance Evaluation on RYU SDN Controller

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Journal of The Institution of Engineers (India): Series B 2024年第2期105卷 203-215页

作者： Ram, Anil Dutta, Manash Pratim Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering NIT Jote Arunachal Pradesh Jote Itanagar791113 India Department of Computer Science and Information Technology Cotton University Pan Bazaar Assam Guwahati781001 India

Software-defined networking (SDN) is software-based networking technology which overcomes the complication tackled by traditional networking structures. The knowledge behind the SDN gives more flexibility to handle network as compared to traditional network since SDN detached the control plane (CP) from the data plane. As the SDN is a software-based technology, evaluation of performance is needed. The heart of SDN is SDN controller which is located in CP. SDN controller is accountable for the monitoring, decision making and investigation of factual time data traffic. Due to substantial rise of distributed real-time applications, there is a prerequisite for an efficient controller in networking sectors, data centers, academic and examination. As a result, it is critical to look at the presentation of a free-source specified controller in order to offer effective traffic routing, which will lead to better resource usage and improved network performance matrices. For network traffic analysis, the study proposes an execution of SDN structural design utilizing an RYU SDN controller. The present spadework assesses the performance of a bespoke network topology based on SDN architecture for device-to-device performance characteristics such as round trip time, bandwidth, and throughput. In the present default network topology for SDN, the simulation results show that our approach outperforms its competitors. © The Institution of Engineers (India) 2024.

关键词： Controllers

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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Improved clustering techniques for paediatric cerebral palsy gait assessment during rehabilitation

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Singhal, Prateek Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India

The gait abnormality may be the cause of various diseases like foot drop, lower back trembling, and osteoarthritis in the human body. The causes may affect body performance. The problem may be solved if we notice it between the ages of 2 and 20. Today's medical research struggles to identify normality and abnormality in children at a young age. The gait abnormalities depend on a complex neurological condition called cerebral palsy. The article proposes an improved fuzzy C-mean-PSO technique and also defines selection criteria for gait patterns, such as optimal number identification gait profiles, mean square error, silhouette coefficient, and Dunn index. The researcher used 156 patients’ data from the available O’Malley gait dataset for experimental purposes. We partitioned 156 patients into 5 different combinations. In the first two combinations, we applied conventional methods, and the next three employed proposed methods. Finally, we found the 91.6% CPI (Cluster Purity Index), that is greater than existing techniques. In the future, we can perform the proposed methods on various datasets. The findings indicate that employing clustering-based gait profiles improved fuzzy C-mean-PSO optimised using these methods can aid in measuring clinical rehabilitation for children with cerebral palsy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Gait analysis Gauss Cuckoo search IPSOFCM PSO Rehabilitation

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SISRR: Semantically Inclined Strategic Learning Model for Software Requirement Recommendation Using Artificial Intelligence 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Shyamalan, Raj Deepak, Gerard Santhanavijayan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering BMS Institute of Technology and Management Yelahanka Bangalore India

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

The following paper proposes the SISRR framework for knowledge centric, semantically inclined, framework for software requirement recommendations. This framework is intelligent driven by integrating semantically inclined techniques with knowledge model. The SISRR model is a metadata driven framework, where the initial requirements are preprocessed, and metadata is generated to intensify the auxiliary knowledge. This is handled by deep learning GRU classifier. Subsequently ontologies are generated from the SVN repository, which is applied along with Bag of Words to enrich the ontologies. The semantic similarity is computed using CoSimRank and Tang Index. Shannons entropy is also computed to measure the information content. The context map is generated using the DBSCAN algorithm in the model. Thus, the best precision, recall and lowest FDR has been achieved by the SISRR model compared to other existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ontology

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A Quantum Approach for Text based Steganography 15

A Quantum Approach for Text based Steganography

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Naik, Darsana A. Pallavi, N. Keerthi, P. Rai, Abhinav Kumar Jha, Adarsh Kumar Bindal, Avaneesh Chauhan, Shubhi Singh M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Impact Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331300579

In or daily lives, maintaining the security of sensitive information has proven to be very difficult. communications sent frequently through a communication channel like the Internet may catch the attention of crackers and hackers, leading to possible attempts to decrypt and reveal the odd communications. With the development of digital communication, the Internet, and cloud-based storage, there is an increasing need for novel and effective methods for protecting the privacy and security of digital data. Steganogra-phy and cryptography are often used together as a solid privacy protection strategy. However, numerous studies on steganography have been established by various academics. Here, a text steganography method has been developed using quantum circuits and XOR-based symmetric cryptography. In this case, the quantum technique also includes boosting security. using a quantum gate truth table to map the logic allows for the embedding of cov-ert and hidden messages. Since text messages are generally regarded as being safe and not requiring further investigation, this technique offers an excel-lent data protection system. Steganography is an art form that can be used to transmit data securely through any form of open communication. © Grenze Scientific Society, 2024.

关键词： Quantum electronics

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