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1st International Conference on Computing, sciences and Communications, ICCSC 2024

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

ISBN: (纸本)9798350353648

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. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance Estimation of Pattern Matching Algorithms Employing DNA Sequence

Performance Estimation of Pattern Matching Algorithms Employ...

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

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

ISBN: (纸本)9798350385199

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. © 2024 IEEE.

关键词： Gene encoding

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A Hybrid Framework for Data Loss Prevention and Detection

A Hybrid Framework for Data Loss Prevention and Detection

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

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

ISBN: (纸本)9798350385199

Modern enterprises face a serious threat from data loss, or the unintentional or undesired disclosure of data. Modern Data Loss Protection (DLP) systems either discover anomalies in regular behavior (anomaly-based) or use signatures of known attack patterns. Though signature-based solutions are effective in accurately identifying known attacks and may thus be used to block them, they are unable to handle unexpected attacks or attackers who choose atypical attack vectors (such as those that are only known to insiders) to execute their attack. However, because they frequently produce a high false positive rate, anomaly-based solutions are not as useful for identifying suspicious activity. In contrast, they can identify undiscovered attacks. Our research presents a hybrid DLP system that integrates anomaly-based and signature-based approaches to facilitate both prevention and detection. Using an anomaly-based engine, the framework automatically builds a model of typical user behavior, enabling it to detect unusual transactions carried out by insiders. This is usually where anomaly-based solutions end. To prevent unwanted transactions from causing harm, our framework goes one step further by using an operator's feedback on alarms to automatically create and update attack signatures. We manage to improve time to detect a data breach was reduced by 67%, improving response efficiency. © 2024 IEEE.

关键词： Information leakage

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Enhanced Fraud Detection in Ethereum Transactions: Fusion of Modified Genetic Algorithms and Deep Learning with Limited Attributes

Enhanced Fraud Detection in Ethereum Transactions: Fusion of...

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

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

ISBN: (纸本)9798350385199

Ethereum smart contracts, the new way of transactions and a popular name in the world of cryptocurrencies, have gathered a huge base of research and scientific attention. They are so helpful that they allow us to eliminate the need for a separate third-party library to allow unknown parties to see contract details on a computer. But since we can all see that online commerce is growing day by day and will continue to grow, it can never be fully free from scams and unethical operations. So, to correctly detect all such unethical and malicious transactions, this paper used a deep learning model. And to further enhance the model, this study used metaheuristic optimization as well. It employs an algorithm called Genetic Algorithm and to provide better optimization in the explorations phase of Cuckoo Search (CS) to achieve its goal of detecting fraudulent transactions. The algorithm covers the loopholes in the CS strategy. Furthermore, to provide strong grounds for research in our paper, this proposed model was compared with various types of approaches such as Light Gradient Boosting Machine, Support Vector Classification, Multi-Layer Perceptron, XGBoost, Logistic Regression, and Random Forest. The proposed model outperforms other significant models like SVC, KNN, LGBM Classifier, RF etc. with the accuracy of 98.6%. © 2024 IEEE.

关键词： Genetic algorithms

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Self-Improved Namib beetle optimization Trained Hybrid Model for Human Activity Recognition 14

Self-Improved Namib beetle optimization Trained Hybrid Model...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Tyagi, Anshuman Singh, Pawan Dev, Harsh Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350335095

Human Activity Recognition is a recognition technique which identify and name the human activities by AI. It collects activity data from devices such as wearable sensors and smart devices etc. Literally, it classifies human activities on the basis of interaction, motion and action. Nowadays, its usability is spread in various applications so, the research on Human Activity Recognition is still ongoing with viable enhancements. Though, this technique needs to solve limitations such as collection and processing of data, stability of hardware and approaches, complexity in activity detection and messed up activities. This work is based on Human Activity Recognition which is trained by the hybrid model of classifiers such as LSTM and Bi-GRU via proposed Self-Improved Namib Beetle Optimization model (SINBO). The activity recognition is detected by two stages: (1) Preprocessing, (2) Feature Extraction and (3) Classification. The extracted features areImproved bag of visual words, Local texton XOR pattern and SLIF are given as an input in classification stage. The hybrid model of LSTM and Bi-GRU is enhanced by optimal tuning of weights via SINBO algorithm. © 2023 IEEE.

关键词： Feature extraction

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Self-Improved Beluga whale optimization algorithm based Optimal Trained Deep Maxout Model for Glaucoma Detection 14

Self-Improved Beluga whale optimization algorithm based Opti...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350335095

Glaucoma is a fast-growing retinal disease, which is a chronic and neurodegenerative disease that affects patients progressively. The neuro-retinal nerve called the optic nerve, which connects the eye to the brain damages the eye due to lack of proper diagnosis. To diagnose this disease, ophthalmologists need to perform various tests. However, taking more tests requires a lot of time and that is expensive. Also, the early stage of glaucoma may not show any vision loss to the affected people. For detecting glaucoma, this paper intends to propose the glaucoma detection model that follows four phases including preprocessing, segmentation, Feature extraction, and glaucoma detection. In the Preprocessing phase, the retinal images are processed by median filtering. Subsequently, subjecting the preprocessed image to the segmentation, where the optic cup will be segmented by the improved U-net model. Subsequently, the features such as cup-to-disc ratio, inferior superior nasal temporal features, fractal features, and proposed Local Gabor features are derived from the segmented image. These extracted features are trained to the optimized DeepMaxout classifier for detection purposes. Finally, a novel self-improved Beluga whale optimization algorithm is proposed for the optimal training of DMN by tuning its optimal weights. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Enhancing Patient Care: A Data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality 3

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Telsang, Supriya Shrikant Singh, Akhya Sanjay Kaur, Simarpreet Hazela, Bramah Singh, Shikha Chittapragada, Hemanand Vishwakarma Institute of Technology Bibwewadi Maharashtra Pune India Shah & Anchor Kutchhi Engineering College Mumbai India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350354171

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of 94.36%. © 2024 IEEE.

关键词： students

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Intrusion detection and prevention system for an IoT environment

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Digital Communications and Networks 2022年第4期8卷 540-551页

作者： Ajay Kumar K.Abhishek M.R.Ghalib A.Shankar X.Cheng Department of Computer Science and Engineering NIT PatnaBiharIndia Faculty of Science Engineering Computing(SEC)De Montfort UniversityDubaiUnited Arab Emirates Department of CSE Amity School of Engineering and TechnologyAmity UniversityUttar PradeshIndia Department of Computer Science Middlesex UniversityLondonUK

Internet of Things(IoT)security is the act of securing IoT devices and *** devices,including industrial machines,smart energy grids,and building automation,are extremely *** the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this *** intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom *** proposed NBIPS inspects network activity streams to identify and counteract misuse *** NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe *** IPS sensors can be installed either in an inline or a passive *** inline sensor is installed to monitor the traffic passing through *** sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

关键词： IDS and IPS Cloud Firewall Denial of service Smart environment Internet of things NBIPS

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Intelligent Farming using Energy Efficient Routing Protocol with Efficient Transmission in Agriculture 6

Intelligent Farming using Energy Efficient Routing Protocol ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Rao, Ashutosh Kumar Nagwanshi, Kapil Kumar Shukla, Manoj Kumar Aswal, Shobha Computer Science & Engineering Amity School of Engineering & Technology Amity University Rajasthan India Computer Science & Engineering Guru Ghasidas Viswavidalaya Raipur India Computer Science & Engineering Amity University Uttar Pradesh India School of Computing Graphic Era Hill University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350398496

Wireless sensor networks (WSNs) were initially developed for research and development purposes, and are now widely employed in a variety of domains such as communication, weather prediction, agriculture, industries, smart physical condition monitoring, and surveillance. In the agriculture sector, in order to increase yields, the IoT-enabled WSN is extensively used to monitor the environment and climatic conditions by employing a variety of sensors to sense data. These types of sensors are fully deployed in the required agricultural environment to improve production yield through intelligent farming and decision making, to find better and necessary information about plants, such as temperature, humidity, and irrigation patterns. In India, Agriculture gives employment to almost 70% of the population and generates 27 % of the GDP (Gross domestic production). This makes agriculture an important area for research and development. This research study is completely based on the surveying of different methods, which are completely based on Internet of Things (IoT) for leveraging betterresults. © 2023 IEEE.

关键词： Energy efficiency

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Cloud Resource Management: Monitoring 5

Cloud Resource Management: Monitoring

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Rawat, Anshika Singh, Shubham Singh, Pawan Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of computer science and engineering GLA University Mathura Uttar Pradesh India

ISBN: (纸本)9798400709418

The management and use of computer resources within enterprises has changed as a result of the emergence of cloud computing as a paradigm shift. With its diverse and comprehensive portfolio of cloud services, including AWS CloudWatch for monitoring, Amazon Web Services (AWS) is at the vanguard of this revolution. In order to fully utilize the capabilities of these platforms and achieve cost- effectiveness, scalability, and optimal performance, proper cloud resource management is essential. This, study offers a thorough investigation of AWS's cloud resource management, with a focus on Particle Swarm Optimization (PSO) integration as a cutting-edge optimization technique. The dynamic and ever-changing workloads seen in cloud computing systems present a special set of issues for resource allocation and optimization. However, finding the ideal compromise between performance and cost-efficiency is still a challenging task. This study examines the changing environment of cloud resource management with an eye toward the future. In order to enable even more intelligent resource allocation decisions, we take into account the possible integration of machine learning and predictive analytics into our PSO framework. We also look at the broader implications of PSO-driven resource management. © 2023 ACM.

关键词： Resource allocation

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