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Blockchain Innovations in Education: Empowering Students with Smart Contracts 2

Blockchain Innovations in Education: Empowering Students wit...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Banu, M. Sheerin Pandian, A. Sevuga Madhavi, Bindu Sasikumar, P. Ks, Vishal Beevi, L Sherin R.M.K. Engineering College Department of Information Technology India Department of Computer Science Karnataka Bengaluru India Andhra Pradesh India Chennai India School of Commerce Vet Institute of Arts and Science Department of Commerce Tamilnadu Erode India R.M.D Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350371055

A lot of people are interested in how blockchain technology can be used in education because it has the potential to change everything. The main point of this study is to investigate how the Internet of Things (IoT), smart contracts, and blockchain technology can be used to give students more power in the classroom. Traditional school systems have a lot of problems when it comes to being open, safe, and effective. When old-fashioned ways of keeping records and managing data are used, wrong information, fraud, and not being able to access data are possible outcomes. In the past, though, studies have been done on separate parts of these technologies. This work suggests a complete plan to fix current issues in the education field that makes use of how blockchain technology, smart contracts, and the IoT can work together. The study uses a mixed-methods approach, which means that it uses both qualitative and quantitative evaluations. The results show that there is an effect that is good for student empowerment because it makes it easier for students to get to learning materials and generally lowers the amount of work that needs to be done. © 2024 IEEE.

关键词： Blockchain Education Technology Internet of Things Smart Contracts Student Empowerment

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Meta-Learned Word Embeddings for Few-Shot Sentiment Classification 7th

Meta-Learned Word Embeddings for Few-Shot Sentiment Classifi...

引用

7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Pandey, Sumit Sharma, Srishti Department of Computer Science School of Engineering and Technology The Northcap University Haryana Gurugram122017 India

ISBN: (纸本)9789819907687

For each new domain, researchers and technologists developing sentiment classification algorithms must gather and curate data. In particular for domains where the features change over time, the work required to annotate these corpora is prohibitive. The idea of training classifiers on a limited set of domains before using them on further domains is explored in this study. One issue is that classifiers frequently perform poorly when the distributions of the training and testing sets of data are different. Another issue is that it is unclear how to choose domains that are suitable for training and testing other domains based on similarities. To train classifiers that generalize and still perform well on test sets that come from various distributions, the authors approach this challenge by making use of numerous, smaller training datasets. The authors suggest using current developments in meta-learning to learn stronger cross-domain embeddings that can generalize for the few-shot learning context to achieve this. This work investigates sentiment classification using few-shot learning. There are a variety of existing approaches to this problem, but recent advances in meta-learning have proven to be quite effective in the few-shot context. For few-shot learning, the authors suggest using MAML (Model Agnostic Meta-Learning) to quickly train better cross-domain embeddings and networks. Our final model performs at a cutting-edge level. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Building of Computationally Effective Deep Learning Models using Attention-Guided Knowledge Distillation 12

Building of Computationally Effective Deep Learning Models u...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Ashwath, V.A. Ayyagari, Anish Sai Deebakkarthi, C.R. Arun, R Arumuga Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350318210

CNNs and Edge computing have proliferated in the last decade. While these technologies are stupendous in their own right, new horizons can be opened up if they were to be combined. Deep and complex CNNs, which are ones that produce groundbreaking accuracies, are not feasible for deployment in a resource constraint environment. The vanguard in model compression is an innovative technique known as Knowledge Distillation. This paper proposes a way to perform knowledge distillation by means of Attention Maps. The generation of attention maps from features is achieved through the use of ATCam. Not only does this method effectively distills knowledge it also nullifies another flaw of CNNs-black-boxiness. This may lead to leapfrogs in Mobile Machine Aided Medical Diagnosis by deploying interpretable complex CNN in small devices. Three ways to combine the feature maps were experimented with in this research work, which are Feature Map Accumulation(FMA), Maximum Intensity Projection(MIPs) and Saliency Maps. The performance of the proposed approaches are tested on PADUFES-20 dataset and all the three approaches provided significant boost in the performance over the traditional transfer learning methods. The best of these methods - saliency maps - gains a accuracy boost of 9.56% over the generic methods of knowledge transfer. Moreover, to one's surprise, the resultant model not only matches the teacher model in accuracy, it exceeds it by 1.52% while being only 43.2% of original teacher model's size. © 2023 IEEE.

关键词： Embedded systems

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Indian Sign Language Generation from Live Audio or Text for Tamil 9

Indian Sign Language Generation from Live Audio or Text for ...

引用

9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Reddy, Bandi Rupendra Rup, Daka Chandra Rohith, Mathi Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350397376

Communication is the key behind the whole human evolution, and there would be nothing possible if the stream for communicating our thoughts to one another were cut off. Language is a significant factor when it comes to conveying one's ideas, thoughts, and information to other people. Sign language is used to communicate hard-of-hearing people's opinions. Every country has different sign languages. In this paper, we have used ISL (Indian Sign Language). To ease communication between ordinary people and deaf/dumb people, we propose the design and implementation of a model that translates a live voice, audio recordings, or text of a native Indian regional language (Tamil) to text and then further matches it to sign language animations from the video animation dataset. The speech is converted to text using two deep learning models LSTM(Long Short Term Memory), Bi-LSTM, and Google API. Then the text is transformed into a sign using ISL (Indian Sign Language) dataset. The proposed models achieved 45%, 65%, and 95% accuracy for LSTM, Bi-LSTM, and Google API, respectively. © 2023 IEEE.

关键词： Long short-term memory

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Named Entity Recognition-based Hospital Recommendation 2

Named Entity Recognition-based Hospital Recommendation

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rajathi, S. Kumar, Reddi Tharun Krishna, Sripathi Vamsi Sabareesh, S. Ethihas, Pandi Eswar Chand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347982

Extracting the intended information from raw text input remains a potential challenge in the biomedical industry. An NER based pre-diagnosis model that uses a general medical description from the patient is proposed to extract the symptoms. In addition, a Machine Learning model that predicts the disease and recommends a hospital is discussed. To develop this pipeline, we collected the BC5CDR dataset, supplemented with manually annotated data, and trained it using several models, including the spaCy blank model, pre-trained BC5CDR model, and transformer models such as the BERT case and RoBERTa. After evaluating the models' performance, we found that RoBERTa outperforms other models with a higher F1 Score of 0.8586 and precisely identifies symptoms, diseases, and chemicals from the medical description. These physical symptoms are used by ML techniques to predict possible disease. Then, using the hospital's data and predicted results, the K-nearest neighbor algorithm suggests nearby, highly rated hospitals based on the user's location, distance, and ratings. This study can potentially enhance medical diagnoses and treatments and provide a promising foundation for future medical consultations and self-diagnosis. © 2023 IEEE.

关键词： Hospitals

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DeepTuber: Sequential CNN-based Disease Detection in Potato Plants for Enhanced Crop Management 5

DeepTuber: Sequential CNN-based Disease Detection in Potato ...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Panshul, Gade Sai Pushadapu, Dilip Reddy, Gona Eswar Kushal Kumar Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350321425

Global potato production is seriously affected by the spread of potato plant diseases, which has far-reaching effects, including decreased crop yield, financial loss and food security challenges. Early detection and accurate diagnosis of these diseases are essential for enabling preventative measures. The proposed architecture combines deep learning and machine learning methods to create disease detection models. The experimental results show that the developed automated disease detection system has achieved an accuracy of 98.83% in identifying potato plant diseases and thus establishing its competence in providing accurate and reliable disease identification. © 2023 IEEE.

关键词： Convolutional neural networks

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Data Address focused Implementation of Low Latency Secure Blockchain for EMRs 3

Data Address focused Implementation of Low Latency Secure Bl...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Kumaran, U. Thangam, S. Nidhin Prabhakar, T.V. Shreyas, S. Krishnan, Vivek R Pranav, S. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350340358

Data privacy is always a concern when it comes to any kind of personal data. In medical domain, the medical data of a patient needs to be more protected and also be accessible. The medical industry is facing the problem of storing and sharing medical data with multiple parties while promising security at the same time. The hospitals don't share their patient data with other hospitals or organizations because of a lack of security in the infrastructure used to transfer this sensitive data. With the introduction of a blockchain based Electronic Medical Records (EMR) may help to solve the issue to a certain extent. From literature it is found that the current blockchain EMR schemas focus on patient-owned models. In this paper, design and development of a web-based software to store patient electronic medical records in a block chain-based back end that is supplied over the internet using Amazon Web Services (AWS) Managed Blockchain is proposed. The major goal is to ensure that the model fits the Confidentiality, Availability, and Integrity (CIA) trinity and performs effectively in benchmarks. In comparison to literature, the proposed block chain demonstrated up to a 65% increase in read throughput, 260%-400% improvement in write throughput on certain work loads, in addition to being exceptionally dependable and secure. The underlying model can be upgraded using cutting-edge algorithms and approaches to lower the model's maintenance costs and raise its efficiency. The proposed model has been compared with already proposed method in literature and tends to give significant results. © 2023 IEEE.

关键词： Blockchain

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Ethereum Fraud Detection: A comparative analysis of supervised learning approach 1

Ethereum Fraud Detection: A comparative analysis of supervis...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Tripathy, Nrusingha Chaudhury, Kumar Surjeet Nibedita, Arpita Nayak, Subrat Kumar Behera, Manjushree Jena, Baidehi Siksha O Anusandhan Deemed to Be University Department of Computer Science and Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India Trident Academy of Technology School of Computing Odisha Bhubaneswar India P.N College Department of Computer Science Khordha India

ISBN: (纸本)9798350355093

Ethereum is an application platform that distributes versions of intelligent contracts to thousands of people globally, utilizing blockchain to decentralize data. Ethereum is a global currency that is used to exchange value without requiring supervision or outside involvement. However, as e-commerce grows, the biggest threat to trade security is the proliferation of illegal activities like phishing, money laundering, and bribery. The need for strong defenses is highlighted by the inherent hazards of blockchain technology, such as the potential for fraud and cyberattacks. The stability and preservation of confidence in the financial system depend on an accessible network that is impervious to these kinds of assaults. Different classification algorithms, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are being used in this work to detect Ethereum fraud. We are utilizing a dataset that includes rows of Ethereum cryptocurrency transactions along with rows of known unauthorized transactions. Notably, the 'XGBoost' model identifies differences that might draw attention and avert possible problems in this task. Supervised learning for Ethereum fraud detection improves the ecosystem's security and integrity. © 2024 IEEE.

关键词： Ethereum

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Prevention Strategy to Detect Reverse Shell using Security Onion in Hoax Shell

Prevention Strategy to Detect Reverse Shell using Security O...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Nikhil, Perla Sai Chadha, Angela Raj Shanmugam, Udhayakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350325188

There are millions of virus variations, making it extremely tough to safeguard a corporation. Intrusion detection and prevention devices can identify malicious behaviour achieved by external or internal attackers by monitoring and analyzing network data. The "connect-back shell," often referred to as a reverse shell, creates a shell session on the target system before accessing the victim's computer. The objective is to establish a connection to a remote computer and reroute the input and output connections of the target system's shell to enable remote access by the attacker. The data is delivered and displayed in the listener's system when the listener executes malicious shell code on the initiator's computer when a connection is made. The Security Onion platform includes administration options such as Secure Shell (SSH) for server and sensor management and remote web client access. The comprehensive packet capture feature of Security Onion gives the security analyst access to host, session, and network data. Security Onion's comprehensive packet capture capability gives the security analyst a wide range of monitoring, analysis, and management options by allowing access to host, session, and network data. The proposed work creates a bogus reverse shell attack that uses Security Onion to carry out the basic functions of a reverse shell and can sniff network communication between the attacker and victim. © 2023 IEEE.

关键词： Attiny 65 Hoax shell Intrusion Detection system NAT Security onion Virus Total Wireshark

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Spatial-Temporal Analysis for Traffic Incident Detection using Deep Learning

Spatial-Temporal Analysis for Traffic Incident Detection usi...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Staby, Solomon Manjusha, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350325188

The domain of Automatic Incident Detection(AID) on roads is predominantly focused on accident detection, lacking the ability to understand specific types of other incidents which may involve accidents. Moreover, existing methods often exhibit limited generalizability, making them susceptible to high error rates when faced with unknown scenarios. To handle these research gaps, this research proposes a deep learning approach that incorporates both the spatial and temporal aspect of videos for the purpose of multiclass video classification in traffic incident detection. By leveraging the modified LRCN network, we enable classification of videos into eight distinct road incident categories. We utilize the Highway incident detection dataset (HWID12) which has more than 2700 videos in different classes. Through rigorous training and comprehensive validation, our approach achieves a precision of 70.30% in the 8-class classification. When considering the dataset as a 2-class classification problem, we achieved an accuracy of 98.02%, which matches the benchmark accuracy on this dataset. These results demonstrate the efficacy of our CNN based framework for automated traffic incident detection, overcoming the limitations of existing methods in terms of specificity and generalizability. © 2023 IEEE.

关键词： deep learning approach LRCN network multiclass video classification traffic incident detection transportation

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