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Enhanced image segmentation for cancer images using sparse based classification framework

Enhanced image segmentation for cancer images using sparse b...

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Disruptive Technologies (ICDT), International Conference on

作者： Atul Kumar Rai Archana Singh Satya Prakash Yadav Research Scholar Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh Campus Amity school of engineering technology Amity University Noida UP Department of Computer Science and Engineering G.L. Bajaj Institute of technology and Management (GLBITM) Affiliated to AKTU Greater Noida India

The correct identification and class of cancerous regions in scientific pictures is vital for diagnosis and treatment planning. This examination recommends an enhanced image segmentation approach with a sparse-based total-type framework for most cancer pictures. Conventional segmentation techniques rely on hand-made functions and require guide tuning, leading to restrained accuracy and generalizability. Our proposed approach uses a sparse representation method, which mechanically learns discriminative functions from the statistics to enhance the accuracy of cancer place segmentation. Moreover, we include a classification framework, primarily based on a help vector device, to refine the segmentation consequences.

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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

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

作者： Pallab Banerjee Sweety Kumari Biresh Kumar Kanika Thakur Kumar Gaurav Purushottam Kumar Dept. of Computer Science Engineering Amity University Jharkhand Ranchi Jharkhand India Dept. of Computer Science Engineering Galgotia University Greater Noida campus Amity School of Information Technology Amity University Jharkhand Ranchi Jharkhand India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

An innovative data structure called a “dynamic tree” has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the “Maximum Flow method,” a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios.

关键词： Runtime Heuristic algorithms Signal processing algorithms Forestry Signal processing Data structures Prediction algorithms Time complexity Optimization Periodic structures

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Deep Learning-Powered Camouflaged Object Recognition Model

Deep Learning-Powered Camouflaged Object Recognition Model

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Riya Tomar Naman Chauhan Chhavi Chauhan Yash Tomar Soumyendra Singh Nishant Chauhan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering CMR University Bengaluru India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation’s role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good.

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FedOnion: FL and Onion Routing-Driven Secure Data Exchange Framework for 5G-IIoT Applications

FedOnion: FL and Onion Routing-Driven Secure Data Exchange F...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nilesh Kumar Jadav Rajesh Gupta Pronaya Bhattacharya Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Amity School of Engineering and Technology Research and Innovation Cell Amity University Kolkata India

The emergence of massive automation has transformed Industrial Internet-of-Things (IIoT) to become adaptive, self-healing, and autonomous. In IloT, the increased volume of data traffic has raised questions about the privacy and security of shared sensor data, resource management, the accuracy of trained models, and the authenticity of network traffic in operation. Thus, conventional security paradigms and centralized learning models are outdated to support the IloT operational space. Modern solutions like federated learning (FL) and onion routing (OR) are integrated into IloT to secure and optimize link communication and improve the computational requirements of central model training. Thus, the paper integrates FL and OR in IloT, and presents a framework FedOnion, where federated classifiers are proposed at intermediate OR circuits, which preserves anonymity and privacy of data sharing among nodes in IloT. In this frame-work, an FL-assisted network traffic classification approach is presented for malicious or non-malicious data requests forwarded to the OR network. Malicious requests are discarded at the next onion router, and it prevents the shared key from getting compromised, as the hash is computed at each hop to signify that data is not tampered with. FL-classifiers divide the overall dataset into small segments, which alleviates the computational burden on OR links and improves the detection rate of malicious data requests. The proposed framework's effectiveness is demonstrated on real-world IloT datasets, based on security and computational parameters. The obtained results indicate the practical viability of the scheme for critical industrial setups which paves the way towards a robust and secured industrial future. The proposed framework is assessed using different performance parameters, such as FL MSE $(10^{-9}$ at 300 epochs), onion circuit compromisation rate (16%), and 5G modulation scheme.

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Movie Recommendation Models Using Machine Learning 5

Movie Recommendation Models Using Machine Learning

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Chawla, Shourya Gupta, Sumita Majumdar, Rana Amity University Amity School of Engineering Technology Department of Computer Science Engineering Noida India Swami Vivekananda University Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9781665403412

A recommendation system is essentially a kind of data filtering. Recommendation systems are very important in today's world since they give users suggestions based on their interests and needs when they are looking for something. Different types of recommendation systems are utilized on numerous platforms and have become an integral feature of a variety of applications. Movie recommendation systems aim to assist movie lovers by recommending what movie to watch without requiring them to go through the time-consuming and complex process of selecting from a vast number of movies ranging from thousands to millions. The goal of this paper is to propose a hybrid movie recommendation system based on the MovieLens dataset. The aim is to combine the user's personalization with the movie's overall features, such as genre, popularity, and so on. A popularity model, a content-based model, a collaborative filtering model, and a model based on latent factors have been used. Each model's hyperparameter tuning, testing accuracy, and evaluation are meticulously carried out. A combined linear model is created by combining the predictions of latent factor based and collaborative filtering methods, which exhibits an improvement in the accuracy of anticipated ratings. To address the constraints of individual models, all the methods are combined to build a hybrid model to get the final suggestion list for users. In terms of quality and diversity of recommendations, the hybrid model performs better than the separate models. The study contains a thorough assessment of each model. © 2021 IEEE.

关键词： Collaborative filtering

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Research Status and Prospect of Anti-slide Piles for Slope Stabilization 1

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1st Belt and Road Webinar Series on Geotechnics, Energy, and Environment, BRWSG 2021

作者： Zhu, Hong-Hu Naveen, B.P. Wang, Jing Cheng, Gang Gao, Yuxin School of Earth Sciences and Engineering Nanjing University Nanjing China Department of Civil Engineering Amity University Haryana Gurgaon India School of Computer Science North China Institute of Science and Technology Langfang China

ISBN: (数字)9789811699634

ISBN: (纸本)9789811699627

The research status of anti-slide piles for slope stabilization is summarized in this paper. The calculation method in pile design is comprehensively reviewed, and special attention is paid to the selection of design parameters according to the actual engineering conditions. The key points in the design and application of anti-slide piles are presented in detail, especially the interaction and deformation coordination between anti-slide piles and soil. Finally, some important issues, such as the soil arching effect and internal force calculation, are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Slope stability

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WallStreetBets: Stock Research Posts Classifier

WallStreetBets: Stock Research Posts Classifier

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Atul Srivastava Rafid Hasan Anuradha Pillai Vijay Shankar Sharma Kamlesh Kumar Singh AUUP Amity School of Engineering and Technology Lucknow India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Department of Computer and Communication Engineering Manipal University Jaipur India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

A discussion platform and financial chat called WallStreetBets is one of the famous places in social media used by investors and traders to talk about stock trading. Our goal is to help in the democratization of stock trading by providing convenient access to expert stock research information. As much as mainstream news outlets are more commonly known, why we have picked this sub-reddit is because while there are people that post nice in-depth research data within the thousands of meme shards here. Therefore, in our dataset will have r/WallStreetBets post which we need to classify into information giving posts and humor based less value news. In that way, readers could filter unwanted posts they might neither need nor would like to read using the said button. They are trading stocks to make day-to- day profits like they would selling candy or app downloads, for example. That being said, with so much noise in the mix it can sometimes be difficult to filter out what articles provide real value vs those that may not really offer any useful or insightful content. We hope to research and classify these threads accordingly making it easier to access only the best of this incredible pool of knowledge. London Letterbox Club strives to make stock research available to everyone, however more importantly our mission is that we inspire an individual with the knowledge and tools needed for them individually as informed intelligent individuals towards trading or investing in Stock. By leveraging the collective wisdom of the r/WallStreetBets community and harnessing the power of data analysis and classification techniques, we strive to create a valuable resource that can benefit traders and investors of all levels, from novices to seasoned professionals.

关键词： Dimensionality reduction Training Technological innovation Accuracy Data analysis Social networking (online) Noise Predictive models Naive Bayes methods Investment

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Methods and Applications for Sentiment Analysis in Online Social Networks

Methods and Applications for Sentiment Analysis in Online So...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Atul Srivastava Kunal Chandra Vijay Shankar Sharma Anuradha Pillai Amity School of Engineering and Technology AUUP Lucknow India Department of Computer Communication Engineering Manipal University Jaipur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With hyped popularity of social media, sentiment analysis, a branch of natural language processing (NLP), has gained significant attention. Social media is a treasure trove for gaining insights into the thoughts, feelings, and opinions of the general public due to the abundance of user-generated content. This work offers a thorough review of sentiment analysis applied in social media, covering its methods, applications, and challenges. It discusses various approaches to sentiment analysis, from deep learning and machine learning models to rule-based techniques. Additionally, it examines the challenges associated with sentiment analysis of social media content, such as context dependence, slang, and sarcasm. The study also explores the various applications of sentiment analysis in social media, including tracking public opinion, political sentiment analysis, customer feedback analysis, and brand monitoring. By exploring these facets, this work seeks to throw light on the current state of the art in social media sentiment analysis and its implications.

关键词： Training Sentiment analysis Analytical models Video on demand Social networking (online) Voting Blogs User-generated content Web sites Monitoring

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Predictive Modelling of Drug Discovery Using Machine Learning

Predictive Modelling of Drug Discovery Using Machine Learnin...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Atul Srivastava Ayush Kumar Srivastava Anuradha Pillai Kamlesh Kumar Singh Vijay Shankar Sharma AUUP Amity School of Engineering and Technology Lucknow India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Department of Computer and Communication Engineering Manipal University Jaipur India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

Machine learning (ML) and artificial intelligence (AI) are revolutionizing drug discovery, expediting and enhancing various stages of the complex process. ML algorithms analyze vast datasets, predict molecular interactions, optimize lead compounds, and identify potential drug targets. This research paper examines ML's applications in drug discovery, focusing on virtual screening, predictive modelling, and precision medicine. Key challenges and future directions for integrating ML into pharmaceutical research are also addressed.

关键词： Machine learning algorithms Precision medicine Computational modeling Predictive models Prediction algorithms User experience Drug discovery Lead compounds Reliability Random forests

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Performance Estimation of Pattern Matching Algorithms Employing DNA Sequence

Performance Estimation of Pattern Matching Algorithms Employ...

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

作者： Pooja Khanna Neelam Manohar Pragya Kumud Tiwari Department of Computer Science & Engineering Amity University Lucknow Lucknow India Department of Computer Science & Engineering Chaudhary Devi Lal University Sirsa India Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow Lucknow India Department of Chemistry M VD College University of Lucknow Lucknow School of Electronics and Electrical Engineering Lovely Professional University India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

The Internet is evolving, with huge amounts of data being generated and stored in terabytes and petabytes. These data contain enormous amounts of information if processed accordingly. In today’s era Information is the oil of the 21st century and analytics is the combustion engine. Analytics on data can be stored in the form of strings. Any string generated has a pattern, analyzing these patterns is the most important thing in this era of information. The information that is stored in a genome database that has been collected over the years from all over the world can be brought to some good use if there is proper analysis done on it. This information can be utilized by a thorough analysis on the sequencing of DNA (De-Oxy-Ribonucleic Acids). DNA is a molecule which stores all the information regarding the development, growth and reproduction of all known living organisms. This simple information can be used to our advantage. The work here compares some of the string pattern-based Algorithms for a real-life application of DNA Sequencing, this pattern matching will be done through the genome database. KMP, Naïve search, BM algorithm, Rabin Karp, Z-Algorithm are few algorithms compared. As the conclusion of work, it is being analyzed comparing the searching complexity of these algorithms that, Algorithms have distinct advantages depending upon the use. The choice of the algorithm could depend upon various other factors including length of the string and the pattern, the type of data, and kind of operations that must be performed. By understating the performance requirements, availability of data, the researcher or the user must make smart choices to choose an appropriate algorithm for their pattern matching tasks. The study here also concludes that KMP is better in terms of matching time for a DNA sequencing application.

关键词： Sequential analysis Databases Signal processing algorithms DNA Genomics Signal processing Reliability Time complexity Bioinformatics Pattern matching

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