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FAMMNPB: design of an Enhanced Forensic Analysis Model with Multidomain Feature Extraction and Advanced Neural Architecture for Permissioned Blockchains 9

FAMMNPB: Design of an Enhanced Forensic Analysis Model with ...

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

作者： Mate, Ashwini Y. Khandare, Kiran S. Hassan, Mohammad Bode, Kiran Nikesh Bode, Nikesh S. Missal, Karishma K. Nagpur India Nagpur India J D College of Engineering And Technology Department of Electronics And Telecommunication Nagpur India J D College of Engineering And Technology Department of Computer Science And Engineering Nagpur India Suryodaya College of Engineering And Technology Department of Computer Science And Engineering Nagpur India J D College of Engineering And Technology Department of Information Technology Nagpur India

ISBN: (纸本)9798350394474

This work introduces a novel approach that leverages multidomain feature extraction coupled with a 1d Convolutional Neural Network (CNN) for robust anomaly detection. To address the challenge of transaction delay predictions, we employ a Bi-directional Gated Recurrent Unit (BiGRU) RNN, for its capability to capture temporal sequences with high precision. For the task of chain splitting, the Teaching-Learning-based Optimization (TLbO) technique is used to ensure the efficient creation of sidechains. Further, we integrated deepSHAP, in tandem with 1d CNN and BiGRU, to enhance the performance of our models, fostering better trust in the results. Our proposed methodology yields notable improvements over existing techniques. As we observed enhancements in the precision of anomaly detection by 4.9%, accuracy in detecting anomalous events by 3.5%, Recall by 4.5% and AUC by 3.9%. These advancements fortify the security posture of permissioned blockchains and optimize their operational efficacy, thus marking a significant improvement in blockchain forensics. © 2024 IEEE.

关键词： Feature extraction

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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Computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 j of stored energy,and energy is consumed at 0.01 j per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet delivery Ratio(PdR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(dE) nodes throughput lifetime

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LEGAL AI: A Tool to help Lawyers Optimize their Performance

LEGAL AI: A Tool to help Lawyers Optimize their Performance

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Almeida, Allan Shah, Shubh Khandor, Neer Bhavesh Agarwal, Neha D.J.Sanghvi College of Engineering Information Technology Mumbai India

ISBN: (纸本)9798350378092

The modern legal environment where quick changes are happening requires integrating advanced technologies to make legal professionals more efficient and effective. The purpose of this project is to propose "LegalAI,"which could solve some of the critical challenges legal software and hardware lawyers currently face, such as document analysis, language translation, and privacy. Legal AI's primary concern is the difficulty and time- intensive nature of reviewing extensive legal documents. By synthesizing and recomposing key points, Legal AI aids the lawyer in obtaining the most important content from lengthier texts as soon as possible. This increases experts' time and productivity. Moreover, the capabilities of legal AI include translation of the language to ensure free interaction among the lawyers from states. It will contribute to greater cohesion and collaboration within the legal cluster. There are several aspects related to the privacy of the client's sensitive information such as redaction. All of the abovementioned aspects contribute to the considerable opportunities of legal AI to improve the performance of the professionals. In general, legal Ai involves the integration of automated document summary, language translation, and privacy protection mechanisms for better performance of the lawyers. © 2024 IEEE.

关键词： differential privacy

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A Machine Learning Based Framework For Bankruptcy Prediction In Corporate Finances Using Explainable AI Techniques

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Informatica (Slovenia) 2025年第15期49卷 15-26页

作者： Maktum, Tabassum Pulgam, Namita Chandgadkar, Vaishnavi Pathak, Prachi Solanki, Aditya Department of Computer Engineering School of Engineering & Technology Anjuman I Islam’s Kalsekar Technical Campus Maharashtra New Panvel India D J Sanghvi College of Engineering Maharashtra Mumbai India Department of Computer Engineering Ramrao Adik Institute of Technology D Y Patil Deemed to be University Maharashtra Navi Mumbai India

Forecasting bankruptcy within corporate finances is an indispensable endeavor crucial for sustaining business growth and fostering stability. The paper presents a methodology to redefine the conventional approach to bankruptcy prediction within corporate finance. Through the adept utilization of advanced machine learning techniques, notably classification models, a dynamic and adaptable framework is established, enabling the systematic categorization of companies based on their bankruptcy risk profiles. Moreover, the methodology addresses the inherent challenge of data bias by integrating oversampling techniques like the Synthetic Minority Over-sampling Technique (SMOTE), thereby ensuring a more equitable representation of minority class samples and bolstering the model’s predictive accuracy. The resulting model delivers timely and precise forecasts of bankruptcy risk, fortified by crucial recommendations such as the Altman Z-Score for vulnerability assessment, debt-to-Equity Ratio for insights into leverage, Quick Ratio for assessing liquidity, and Explainable AI Techniques like SHapley Additive exPlanations (SHAP) analysis for transparent interpretations. This comprehensive approach equips stakeholders with tailored recommendations, empowering them to proactively safeguard their organizations’ financial well-being and avert the perils of bankruptcy. The comparative analysis presented in paper demonstrates that the proposed method assesses the bankruptcy risk more accurately. The integration of Explainable AI techniques and key financial metrics helps the stakeholders to take vital decisions about corporate finances. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Risk assessment

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Review Based on Blockchain and Financial Transactions Related to Cryptocurrencies Across Globe 11

Review Based on Blockchain and Financial Transactions Relate...

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11th International Conference on Emerging Trends in engineering and technology - Signal and Information Processing, ICETET - SIP 2023

作者： Sonekar, S.V. Bejjaniwar, Saikiran dandekar, Shejal Pal, Sarthak Karwade, Pratik J D College of Engineering and Management Department of Computer Science and Engineering Nagpur India J D College of Engineering and Management Department of Information Technology Nagpur India

ISBN: (纸本)9798350348422

Blockchain is one of the most interesting and exciting field of technology today it provides a decentralized network for anyone who is willing to store data on it as blockchain is relatively new there is still experiments going on it to reveal its true potential. One of the applications of blockchain which has gone incredibly popular is cryptocurrency and also blockchain payment system. In today's blockchain payment system there are few components which are required for a successful transaction such as nodes, ledger, wallet, nonce and hash. Payment gateways are one of the first blockchain application that provide a decentralized way to do transactions we will also discuss some of the ledger applications of blockchain. We carefully selected the most recent research papers in this review paper. We will be reviewing various research paper and they take on blockchain technology. © 2023 IEEE.

关键词： Smart contract

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deep Learning Framework for the Prediction of Childhood Medulloblastoma

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Computer Systems Science & engineering 2023年第7期46卷 735-747页

作者： M.Muthalakshmi T.Merlin Inbamalar C.Chandravathi K.Saravanan Department of Biomedical Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electronics and Instrumentation Engineering Saveetha Engineering CollegeChennaiTamil NaduIndia Department of Information Technology J.J.College of Engineering and TechnologyTrichyTamil NaduIndia Department of Information Technology R.M.D Engineering CollegeKavaraipettaiChennaiTamil NaduIndia

This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological ***,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep *** introduction of pooling layers in the architecture reduces the feature *** extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network *** performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and *** results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.

关键词： Brain tumour childhood medulloblastoma deep learning histopathological images medical image analysis

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Optimal Key Generation for Privacy Preservation in Big data Applications Based on the Marine Predator Whale Optimization Algorithm

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Annals of data Science 2025年第2期12卷 539-569页

作者： jadhav, Poonam Samir Borkar, Gautam M. Department of Computer Engineering Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Nerul Navi Mumbai400706 India D J Sanghvi College of Engineering Vile Parle Mumbai400056 India Department of Information Technology Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Nerul Navi Mumbai400706 India

In the era of big data, preserving data privacy has become paramount due to the sheer volume and sensitivity of the information being processed. This research is dedicated to safeguarding data privacy through a novel data sanitization approach centered on optimal key generation. due to the size and complexity of the big data applications, managing big data with reduced risk and high privacyposes challenges. Many standard privacy-preserving mechanisms are introduced to maintain the volume and velocity of big data since it consists of massive and complex data. To solve this issue, this research developed a data sanitization technique for optimal key generation to preserve the privacy of the sensitive data. The sensitive data is initially identified by the quasi-identifiers and the identified sensitive data is preserved by generating an optimal key using the proposed marine predator whale optimization (MPWO) algorithm. The proposed algorithm is developed by the hybridization of the characteristics of foraging behaviors of the marine predators and the whales are hybridized to determine the optimal key. The optimal key generated using the MPWO algorithm effectively preserves the privacy of the data. The efficiency of the research is proved by measuring the metrics equivalent class size metric values of 0.03, 185.07, and 0.04 for attribute disclosure attack, identity disclosure attack, and identity disclosure attack. Similarly, the discernibility metrics value is measured as 0.08, 123.38, 0.09 with attribute disclosure attack, identity disclosure attack, identity disclosure attack, and the Normalized certainty penalty is measured as 0.002, 61.69, 0.001 attribute disclosure attack, identity disclosure attack, identity disclosure attack. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sensitive data

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Edge Computing-Based Heart Rate Monitoring System using RNN and LSTM 14

Edge Computing-Based Heart Rate Monitoring System using RNN ...

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

作者： Rao, Sannidhi Kulkarni, Shreya Mehta, Shikha Tawde, Prachi D. J. Sanghvi College of Engineering Dept. of Information Technology Mumbai India D. J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9798350335095

Heart rate monitoring is a simple and effective method for identifying the early signs of cardiovascular diseases (CVds). The paper proposes using edge computing as an alternative, where data can be stored and analyzed at the edge, resulting in a more efficient and effective system. The paper includes the design of a real-time healthcare monitoring system that collects data from IoT/wearable devices, analyzes it through deep learning algorithms to diagnose and predict abnormalities, and provides necessary actions to patients/clinicians to save lives immediately. The system architecture is designed to provide a seamless and accurate heart rate monitoring experience for the user, with the use of edge computing, RNN algorithm, and LSTM model training, which can improve the quality of life for patients with heart-related conditions. © 2023 IEEE.

关键词： Edge computing

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An Experimental Investigation to determine the Optimal Nanomaterial for Coating a Solar Thermal Absorber Panel 9th

An Experimental Investigation to Determine the Optimal Nanom...

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9th International Conference on Advances in Energy Research, ICAER 2023

作者： Katekar, Vikrant P. Rao, Anand B. Sardeshpande, Vishal R. Centre for Technology Alternatives for Rural Areas Indian Institute of Technology Bombay Mumbai India J D College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819754182

Most solar thermal systems use dull black absorber plates that absorb sun energy. Nanoparticles’ enormous surface area and increased thermal conductivity promote heat transmission, thermal stability, heating, and cooling. This research identifies the best nanoparticle kind and proportion to mix in dull black absorber plate paint to increase heat transfer from the absorber plate. This exploration employs 10, 20, 30, 40, and 50% aluminium oxide, copper oxide, and titanium dioxide nanoparticles by mass. dull black paint with 30% aluminium oxide nanoparticles had the highest average temperature of 55.3 °C in the open environment. This plate also had the largest radiation heat transfer (15 W) and average absorber plate-atmosphere temperature differential (20.9 °C). This study found that dull black paint with 30% aluminium oxide nanoparticles is best for solar thermal device absorber plates. This experimental research advises using 30% aluminium oxide mixed with dull black paint to boost the thermal performance of the absorber plate of solar stills, dryers, cookers, and air heaters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Titanium dioxide

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2024年第17期84卷 1-21页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined deep Q-Learning (CdQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CdQL detects anomalies accurately. In addition, the CdQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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