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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Suresh Kumar Natarajan G S Pradeep Ghantasala S. Anthoniraj Pellakuri Vidyullatha R Rajesh Sharma U Ananthanagu School of Computer Science and Engineering Jain (Deemed-to-be-University) Ramanagara India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

In this research, a explore the realm of medical imaging, specifically focusing on early brain pathology detection through the lens of deep learning. The primary objective is to optimize SqueezeNet, lightweight convolutional neural network architecture, for brain pathology detection using CT imaging. The motivation stems from the critical need for accurate and swift identification of brain abnormalities, with potential implications for patient outcomes. The meticulously preprocess a dataset of CT images of the human brain to enhance the model’s ability to discern subtle pathology patterns. SqueezeNet, known for its efficiency, is employed, and optimization techniques during training enhance its performance. These findings suggest SqueezeNet’s potential as a valuable tool in the medical field, offering a balance between efficiency and diagnostic accuracy. Throughout the discussion, A critically analyzes the implications of results, drawing comparisons with existing literature and addressing any encountered limitations. In this research contributes to the discourse on leveraging deep learning for brain pathology detection and the experimental results showcase promising outcomes, with a test accuracy of 78%. Precision, recall, and F1-score metrics indicate the model’s proficiency in distinguishing between different pathologies: 87% for Hemorrhagic, 70% for Ischemic, and 79% for Normal cases. Confusion matrices and ROC curves provide comprehensive insights into the model’s classification capabilities. The optimized SqueezeNet application demonstrates promise in enhancing diagnostic capabilities, with the obtained results providing valuable insights for future advancements in the intersection of deep learning and medical imaging, potentially impacting patient care and outcomes positively.

关键词： Deep learning Training Measurement Pathology Accuracy Computed tomography Signal processing Brain modeling Optimization Medical diagnostic imaging

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Retraction Note: Photovoltaic fuzzy based modelling on defining energy efficient solar devices in industry 4.0

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Kumar, T. V. V. Pavan Taranath, N. L. Rahul, R. Shekara, G. Chandra Sapra, Pooja Prabu, R. Thandaiah Metwally, Ahmed Sayed M. Kalam, M. A. Department of Electrical and Electronics Engineering KG Reddy College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Mathematics BMS College of Engineering Bengaluru India Department of Information Technology Parul Institute of Engineering and Technology Parul University Limda India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Mathematics College of Science King Saud University Riyadh Saudi Arabia School of Civil and Environmental Engineering FEIT University of Technology Sydney Ultimo Australia

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Exploring Shopping Opportunities and Elevating Customer Experiences Through AI-Powered E-Commerce Strategies 4th

Exploring Shopping Opportunities and Elevating Customer Expe...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Maurya, Nitu Singh, Jagendra Singh, Navneet Pratap Chaudhary, Anusha Patil, Sambhajiraje Tiwari, Mohit Department of Management KCC Institute of Legal and Higher Education Greater Noida India Alliance School of Advanced Computing Alliance University Bengaluru India School of Computer Science Engineering and Technology Bennett University Greater Noida India Delhi-NCR Ghaziabad India Dr. D. Y. Patil Vidyapeeth Centre for Online Learning Pimpri Pune India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (纸本)9789819769919

This research explores the efficacy of clustering algorithms in enhancing customer experiences within the e-commerce landscape. Through experiment trials utilizing K-means and DBSCAN clustering techniques, valuable insights have been gleaned. The trials yielded silhouette scores ranging from 0.55 to 0.72, indicating moderate to good clustering quality across different experiments. In K-means clustering, the number of clusters varied from 3 to 6, with inertia values spanning approximately 722.4–1456.8. Conversely, DBSCAN clustering resulted in varying cluster numbers, ranging from 2 to 4, contingent on the combinations of epsilon and min_samples values explored. These findings underscore the significance of judiciously selecting clustering algorithms and parameter settings to achieve meaningful segmentation of e-commerce data. Effective utilization of clustering algorithms empowers businesses to discern valuable insights into customer behavior, preferences, and patterns. Consequently, businesses can tailor their strategies to deliver personalized experiences, targeted marketing campaigns, and optimized product recommendations. This research propels the exploration of additional clustering techniques and parameter refinements for enhanced clustering performance in e-commerce applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marketplaces

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The smart Cross-Layer design for Congestion Control in Wireless Networks 15

The smart Cross-Layer Design for Congestion Control in Wirel...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Manju Bargavi, S.K. Kusum, Kriti Mishra, Abhinav Dhabliya, Dharmesh Jaison, B. Sunrise University Department of Computer Science & Engineering Alwar301028 India University Department of Computing Sciences And Information Technology Karnataka Bangalore India Symbiosis International University Department of Communication Design Pune India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India R.M.K.Engineering College Department of Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

The clever pass-layer layout for congestion management in wireless networks is a complete approach that leverages the interaction between community layers to develop efficient, dependable, and cozy Wi-Fi networks. This design combines the transport and MAC (media access control) layer protocols, which include TCP (transmission manage protocol) and IEEE (Institute of Electrical and Electronics Engineers) 802.11, respectively, so that it will manage the flow of records and decrease congestion. It considers both packet-stage information, including packet arrival, inter-packet arrival, and packet size, as well as bodily layer parameters, including channel country information, multi-user variety, and interference. Also, it addresses the ISPs' necessity of changing transmission charges and granting the proper stability among throughput and honest distribution of assets to defend different programs. This layout should respond to modifications within the network quickly and efficiently to obtain the favored quality of service (satisfactory service). Additionally, the cross-layer layout needs to be flexible and sufficient to be extended to modern-day and future communique networks while configurable to specific varieties of networks for better deployment. © 2024 IEEE.

关键词： Combines Information Management Modifications Networks

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Towards a Healthier Online Environment: Machine Learning for Detecting and Addressing Cyberbullying Comments

Towards a Healthier Online Environment: Machine Learning for...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： G S Pradeep Ghantasala P Vidyullatha Rajesh Sharma R U Ananthanagu Akey Sungheetha Kumar Dilip Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Dept. of CSE Koneru Lakshmaiah Education Foundation Guntur India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

With the internet enduring to engage in a momentous part in our lives, it is crucial to prioritize maintaining inclusive and positive interactions on internet platforms. With the rise in internet usage, platforms like Twitter have become a hub for individuals to freely express their thoughts and engage with others. However, this increased user base has also led to an increase in cyber bullying comments which could include hate speech, harassment, or assault language. Sentiment analysis comprises consuming natural language processing methods to consider the sentimentality conveyed with a slice of text, like as a comment or a tweet. It can help identify whether a comment is positive, negative, or neutral, allowing platforms to take appropriate action. This paper focusses on Machine Learning Models to deploy sentiment analysis to classify cyber bullying comments and promoting positive interactions on internet platforms. By leveraging this technology, platforms like Twitter can take proactive measures to adopt a protected and more comprehensive online environment, ultimately enhancing the user experience for everyone involved.

关键词： Support vector machines Sentiment analysis Accuracy Blogs Cyberbullying Machine learning Transformers Data models Classification algorithms Bayes methods

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Detection of False Information in Digital Media Using Blockchain technology

Detection of False Information in Digital Media Using Blockc...

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Electrical Power engineering, Communication and Computing technology(ODICON), Odisha International Conference on

作者： Aswini Kilaru L V Narasimha Prasad G S Pradeep Ghantasala U Ananthanagu Suresh Kallam Rajesh Sharma R Department of CSE Institute of Aeronautical Engineering Hyderabad Telangana Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Center of Excellence in Immersive Technologies Alliance University Bengaluru India School of Computer Science and Engineering Jain Deemed to be University Bengaluru India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The spreading of false information over the internet, highly impacts society and causes social and economic loss to the nation. One of the most important factors for the increase in the number of misleading information is that all the users of any particular digital platform usually do not verify the authenticity of the information. The principal impartial of this proposed work is to afford a solution to society to reduce the social and economic losses caused by spreading false information circulated digitally. Blockchain is a disseminated, decentralized network that conducts peer-to-peer transactions, meaning that it does not have a central entity to operate. Here we present a literature survey on various approaches towards this problem statement and investigate a Blockchain-based solution that focuses on all types of digital content by providing the end user to validate the information along with the evidence for data that was collected at each step

关键词： Surveys Economics Power engineering Neural networks Machine learning Blockchains Peer-to-peer computing Security Reliability Fake news

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Bio-Inspired Optimization Algorithm on Cloud based Image Retrieval System using Deep Features

Bio-Inspired Optimization Algorithm on Cloud based Image Ret...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Vijayakumar, T. Ramalakshmi, K. Priyadharsini, C. Vasanthakumar, S. Shaina Sharma, Abhishek Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Alliance College of Engineering and Design Department of Computer Science and Engineering Karnataka Benglure India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India GNA University Department of Computer Science and Engineering Punjab Phagwara144401 India Shri Vaishnav Vidyapeeth Vishwavidyalaya Madhya Pradesh Indore India

ISBN: (纸本)9781665489621

With the tremendous improvement of cloud computing (CC) technique, increasing count of users choosing to outsource image data to the clouds. Cloud resource provider decreases the storing problem on local hardware by outsourcing massive image database to cloud server and exploiting the cloud computation ability to image processing application. Learning effective feature representation and similarity measures are essential for the performance of content-based image retrieval (CBIR). Despite wide-ranging research efforts for decades, it remains a challenge that significantly hinders the success of real-time CBIR system. Since the trail and error hyperparameter selection is a tedious process, metaheuristic optimization can be used. This paper introduces an Evolutionary Optimization Algorithm for Cloud Based Image Retrieval System (EOA-CIRS) technique. The presented EOA-CIRS technique derives a new CBIR model for retrieving highly related images. To obtain this, the EOA-CIRS technique initially employs MobileNetv3 model as a feature extractor, which derives features from the query and database images. Next, the bamboo forest growth optimizer (BFGO) approach was applied as a hyperparameter optimizer to appropriately tune the hyperparameters of the MobileNetv3 approach. Finally, Euclidean distance based similarity measurement is utilized for retrieving the related images. The experimental validation of the EOA-CIRS model on Corel10K dataset demonstrates the effectual efficacy of the EOA-CIRS model over other recent approaches. © 2022 IEEE.

关键词： Search engines

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Enhancing Network Security Against APTs Through SVM-Based Network Traffic Analysis: Identifying Anomalies in Communication Flows 1

Enhancing Network Security Against APTs Through SVM-Based Ne...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Rathor, Ketan Keerthika, V. Sunanda, Karkala Renuga, K. Shobana, A. Anusuya, M. GyanSys Inc Mumbai India Alliance University Department of CSE Bangalore India Malla Reddy College of Engineering Department of CSE-AIML Maisammaguda Dulapally Secunderabad India St.Joseph's Institute of Technology OMR Department of Mathematics Chennai India Sri Krishna Arts and Science College Department of Computer Applications Coimbatore India Indra Ganesan College of Engineering Department of Physics Tamilnadu Trichy India

ISBN: (纸本)9798350365337

Novel approaches to strengthen network security are required in the dynamic field of cybersecurity due to the increasing prevalence of Advanced Persistent Threats (APTs). To combat APTs, this study builds and tests a high-tech network traffic monitoring system that uses a Kernel Function to train a Nonlinear Support Vector Machine (SVM). In terms of offering thorough and precise threat detection, current models frequently fail. The impressive accuracy of 92%, precision of 88%, recall of 94%, and F1-Score of 0.95 demonstrate the better performance of our suggested system. An essential component of real-time threat detection, the connection with Apache Spark MLlib guarantees rapid processing of large-scale datasets and scalability. In a groundbreaking move, this study integrates distributed computing with nonlinear support vector machines (SVMs), creating an adaptive and resilient approach that surpasses previous models in APT detection in terms of accuracy and dependability. Our proposed model stands out from the competition thanks to the metrics that have been given and shown in a detailed bar chart. In addition to developing a new method for APT detection, this study establishes a new standard for the effectiveness of network security, which will allow for more robust cybersecurity frameworks to withstand changing threats. © 2024 IEEE.

关键词： Scalability

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AI Powered Virtual Job Interview Simulator Using Natural Language Processing

AI Powered Virtual Job Interview Simulator Using Natural Lan...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： G Shivaji Rao M. Jaiganesh Mohamed Anas K Mohamed Asfaq Ali A Nivash R Praveen Kumar Parida Department of Computer Science and Design Karpagam College of Engineering Coimbatore Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Interviews are a crucial gateway to employment in today's highly competitive job market. However, many job seekers face challenges due to inadequate preparation resulting from limited access to realistic and tailored practice sessions. Traditional mock interviews with career counselors or peers often lack constructive feedback and fail to replicate actual interview dynamics. The AI-Driven Virtual Job Interview Simulator addresses this gap by utilizing advanced technologies, particularly Natural Language Processing (NLP), to create an interactive, real-time interview practice platform. This simulator generates role-specific questions aligned with the user's desired career path and evaluates responses in real-time through sentiment analysis and coherence assessments. It offers constructive, personalized feedback that highlights both strengths and areas for improvement. The adaptive learning design of the simulator continuously adjusts question difficulty based on user performance, ensuring a seamless progression. Over time, it learns from individual user behaviors to enhance its feedback, thereby making the experience increasingly personalized and effective. With a user-friendly interface, the system requires no technical proficiency for operation. By combining practicality with cutting-edge AI technology, this innovative tool empowers users by instilling the confidence and skills necessary to excel in interviews, effectively bridging the gap between preparation and employment. The simulator represents a transformative solution for career preparedness, revolutionizing the interview preparation process.

关键词： Training Technological innovation Sentiment analysis engineering profession Employment Natural language processing Real-time systems Interviews Artificial intelligence Speech to text

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Integrated IoT-Based Smart Home Security System: Real-Time Hazard Detection and Alerts Using Multi-Sensor Fusion

Integrated IoT-Based Smart Home Security System: Real-Time H...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sushma, A.M.S.V. Aswini Priyanka, A. Lakshmi, A.N.G. Khatri, Narendra Sharma, Harish S R K R Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Bhimavaram India S R K R Engineering College Department of Computer Science and Design Engineering Andhra Pradesh Bhimavaram India Sir C.R. Reddy College Engineering Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Manipal Acacdemy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal University Jaipur School of Computer Science and Engineering Department of Artificial Intelligence and Machine Learning Jaipur India

ISBN: (纸本)9798350390049

This paper presents an innovative IoT-based smart home security system that integrates multiple sensors to detect various hazards and provide real-time alerts. Unlike traditional single-function systems, this solution combines motion detection via a PIR sensor, gas leak detection using an MQ-2 sensor, and fire detection through an IR flame sensor into a single, affordable package. The Arduino UNO serves as the central controller, interfaced with a GSM module for instantaneous SMS notifications and supported by an on-site buzzer alarm. Extensive testing in diverse environments demonstrates the system's exceptional performance, detecting intrusions in under 2 seconds and reporting threats with approximately 89% accuracy, while minimizing false alarms. The integration of smart power management techniques reduces energy consumption compared to conventional systems. This approach not only enhances home security but also offers a scalable and easily deployable solution for both residential and commercial settings. This work addresses the urgent need for an affordable, multi-functional smart home security system and provides a robust framework for future IoT-driven safety solutions. © 2024 IEEE.

关键词： Smart homes

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