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An Implementation of Blockchain technology in Combination with IPFS for Crime Evidence Management System

An Implementation of Blockchain Technology in Combination wi...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shilpa, C. Shanthakumara, A.H. Siddaganga Institute of Technology Dept. of Computer Science & Engineering Tumakuru India

ISBN: (纸本)9798350348217

Crime is an unlawful act and is punished by the authority, in order to prove crime, evidence is necessary. Evidence obtained from crime scene is crucial as it act as proof of crime, Digitalization of evidences is need of hour. During the entire process of investigation heterogenous format of data is generated and integrity of the sensitive data has to be maintained as sensitive data passes through the various levels of intermediaries forming Chain of Evidences (CoE). Evidence needs to be tramper proof and should be protected from any kind of alterations. In order to build strong system with immutability, integrity, and legitimacy features blockchain technology is more suitable. The digital evidence can be transferred in a transparent way between the parties involved without any central authority using blockchain technology. This paper focuses on how blockchain based solutions can help in building a strong secure system. The system is implemented using Ethereum platform to achieve integrity, immutability transparency as well as tampering can be identified by any one at any time. © 2023 IEEE.

关键词： Smart contract

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification 26

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

引用

26th International Conference on computer and Information technology, ICCIT 2023

作者： Ahmed, Tanvir Mou, Farzana Sharmin Engineering Dhaka University of Engineering & Technology Dept. of Computer Science Dhaka Gazipur Bangladesh University of Birmingham Dept. of Computer Science Birmingham United Kingdom

ISBN: (纸本)9798350359015

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security. © 2023 IEEE.

关键词： Attention Mechanisms Crop Leaf Disease Identification Deep Learning DeepLeaf-Attentive MobileNet Transfer Learning

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Vulnerability in Cell Scheduling Function for 6TiSCH Networks: Impact and its Mitigation 20

Vulnerability in Cell Scheduling Function for 6TiSCH Network...

引用

20th IEEE India Council International Conference, INDICON 2023

作者： Tapadar, Karnish N A Sakre, Aditya Kumar Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350305593

The 6TiSCH standard is widely adopted for Industrial IoT applications, offering low latency and high reliability. Cell scheduling plays a critical role in 6TiSCH networks by determining packet transmission schedules in terms of timing and channel allocation. The IETF introduced the Minimal Scheduling Function (MSF) and the 6top protocol to facilitate schedule management dynamically. This paper investigates the vulnerabilities associated with cell scheduling and the 6top protocol in 6TiSCH networks. We have demonstrated the existence of a cell depletion attack which can be launched by a compromised node, and its potential to disrupt the proper functioning of cell scheduling. This attack adversely affects transmission reliability, end-to-end latency, and energy consumption of victim nodes and their child nodes. To address the impact of this attack, we have proposed a defence mechanism which can be easily incorporated into the MSF scheduling function. Our approach incorporates parent-child authentication and validation measures to ensure that only legitimate nodes participate in the scheduling process. Through extensive simulations, we have verified that the proposed cell depletion attack significantly degrades network performance and increases the energy consumption of victim nodes by 41% in a 60-node network compared to normal functioning of scheduling function, leading to a significant decrease in device lifespan. However, when our defence mechanism is implemented, we observe a significant improvement in network performance even in the presence of the attacker. © 2023 IEEE.

关键词： 6P 6P Vulnerabilities 6TiSCH Network Link Scheduling Minimal Scheduling Function

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Detecting Ransomware Threats Using Machine Learning Approach 8

Detecting Ransomware Threats Using Machine Learning Approach

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shetty, Sanmitha Chitharanjan Bastian, Rose Khader, Sarah Abdul St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart BAT - Smart Budget Analyzer and Tracker 3

Smart BAT - Smart Budget Analyzer and Tracker

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Shelke, Sahil Shingre, Manish Lebisha, Sahaya Shaikh, Shainila Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Maintaining your daily expenses manually by entering all the details of the items purchased can often feel like an intimidating task. This research discusses the difficulties in budget tracking and suggests a fresh budget tracking program to simplify difficulties such as maintaining the expenses manually using pen and paper, spreadsheet or a diary. Departing from these traditional manual approaches the authors have proposed an innovative solution for this tedious task. In this research paper, we have proposed an automated system that combines Optical Character Recognition and Machine Learning to tackle the challenges associated with transforming physical bills into digital format. The key feature of the application includes optical character recognition which will scan the bill and extract all the items present on the bill and automatically categorize the items into various categories where the item belongs. The application also provides some additional features such as future expense prediction and a detailed report generation. In this study, authors emphasizes the significance of effective budget tracking in the modern world and offers a creative solution by creating a user-centered budget tracking application. We hope that by meeting the needs of the average customer and providing a flawless experience, our application can considerably enhance people's financial well-being. © 2023 IEEE.

关键词： Optical character recognition

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Human Skin Diseases Detection and Classification using CNN 3

Human Skin Diseases Detection and Classification using CNN

引用

3rd International Conference on Electrical, computer and Communication Engineering, ECCE 2023

作者： Ahmed, Shamim Abiduzzaman, M.D. Rajib, Mahedi Hasan Rahaman, Rafat Hussain, Salam Rashid, Hafsha Binta Wadud, Anwar Hussen Amir-Ul-Haque Bhuiya, T.M. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

Recently skin disease has turned into a threat to normal human life when it becomes cancer. If there do any negligible steps during treatment it will become severe and also may cause the reason of human death. So, necessary steps in an early stage of the diagnosis are mandatory to avoid the worst scenario like human death, especially when the disease is Melanoma skin cancer. Because the human death rate of Melanoma skin cancer has become high during this recent era. To detect skin cancer disease there is some common treatment strategy which is done by expert healthcare professional like doctor or dermatologist, as well as these procedures are also very lengthy and time-consuming. Therefore, recently some automated computer vision or image processing with artificial neural network-based methods are introduced but during the implementation level, these methods are highly cost-effective thoughthey provide better accuracy during prediction. In this paper, we proposed a deep learning-based CNN method for classifying skin diseases. To implement our proposed model, we gathered approximately 2102 image datasets from a massive repository of diverse source images, particularly dermatoscopic images of pigmented lesions. Our experiment results show an 89.45 percent accuracy in detecting skin cancer disease. We hope our proposed method will help to diagnose this skin cancer disease more efficient way than other methods that have already been used. © 2023 IEEE.

关键词： Diagnosis

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Systematic Approach to Analyze The Avast IOT-23 Challenge Dataset For Malware Detection Using Machine Learning 18

Systematic Approach to Analyze The Avast IOT-23 Challenge Da...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Jahangir, Muhammad Talha Wakeel, Muhammad Asif, Humza Ateeq, Ahsan Mns University of Engineering and Technology Dept. of Computer Science Multan Pakistan

ISBN: (纸本)9798350328172

The integration of the Internet of Things (IoT) becomes more ingrained in our lives, it has gained significant importance for manufacturing companies. By enabling real time monitoring of machine status, evaluating product quality, and observing environmental conditions within factories. IoT empowers managers to make well considered decisions that reduce risks and alleviate losses. However, the growing reliance on IoT devices and services has also given rise to concerns about security and safety due to possible anomalies within IoT networks. Swiftly identifying and resolving these anomalies are crucial to prevent potential harm or losses. The proposed method introduces an innovative approach that leverages machine learning and deep learning techniques for the rapid classification of malicious software attacks on IoT networks. The central goal of this research is to conduct a thorough examination of the Avast IoT-23 dataset with the aim of pinpointing the most effective algorithm in terms of performance and efficiency. In this proposed methodology, the Decision Tree (DT) algorithm stands out as the most efficient and effective choice due to its high accuracy and low time complexity cost. © 2023 IEEE.

关键词： Internet of things

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Towards an integration management maturity 49

Towards an integration management maturity

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49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

作者： Hyrynsalmi, Sonja M. Olsson, Helena Holmstrom Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350342352

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management. © 2023 IEEE.

关键词： Integration

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Smart Posture Analyzer for Exercise 6

Smart Posture Analyzer for Exercise

引用

6th IEEE International Conference on Advances in Science and technology, ICAST 2023

作者： Dhaigude, Siddharth Panigrahi, Hrishikesh Godse, Sahil Shaikh, Phiroj Dept. of Computer Engineering Don Bosco Institure of Technology Mumbai India

ISBN: (纸本)9798350359817

Going to the gym on your own is, of course, possible, but a personal trainer can really help you increase your endurance. In this day and age, many people rely on YouTube tutorial videos to figure out what works best for them. However, if done unsupervised or incorrectly, it can be ineffective and potentially dangerous. During exercise, sudden movements and poor posture can result in temporary or permanent disability. So, we devised this smart exercise posture analyzer concept. By alerting the user and providing alternative actions, the system warns the user about poor posture. This would allow the user to focus on the exercise rather than the mobile screen. The idea is to track the body posture of the user while they are doing exercise and recommend changes simultaneously. The system will also keep track of reps the user is doing. The proposed system does not consider more than 3 exercises. Algorithms based on machine learning are used to predict and guide users when performing specific workouts. © 2023 IEEE.

关键词： Long short-term memory

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Development of Convolutional Neural Network (CNN) Method for Classification of Tomato Leaf Disease Based on Android 1

Development of Convolutional Neural Network (CNN) Method for...

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1st IEEE International Conference on Smart technology, ICE-SMARTec 2023

作者： Nathanael, Otto Hutagalung, MacLaurin Gamaliel, Yoyok Harapan Bangsa Institute of Technology Dept. of Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350306156

Tomato has become one of the vegetable fruit plants that Indonesian people widely cultivate. In cultivation activities, tomato cultivators will face diseases and pests that affect growth, such as diseases on tomato leaves. With multiple types of tomato leaf diseases, it is challenging for a cultivator to distinguish between them. This research aims to provide cultivators with valuable information about tomato leaf diseases and how to address them using Android-based disease detection. The tomato leaf disease detection application was developed on the Android platform that uses the camera as its main feature to be widely used by the community. TensorFlow Lite is developed as a model for tomato plant disease detection by implementing the Convolutional Neural Network (CNN) method with a total of 9,000 sick and 1,000 healthy tomato leaf datasets. The accuracy, loss, and confidence score are calculated. The system was tested using 50 Epochs, with a dataset comparison scenario of 80:10:10 when training the model. The results obtained by performing five tests show an average accuracy of 92.31 % and an average loss of 24.81 %, with a confidence score of 80-95%. © 2023 IEEE.

关键词： Convolutional neural networks

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