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Adaptive Malware Detection in Android: An Active Learning and XGBoost Approach 5

Adaptive Malware Detection in Android: An Active Learning an...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prattipati, Anshitha Saravanan, S. Veluchamy, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376685

The growing dependence on mobile devices like smartphones has caught the consideration of cybercriminals. It creates numerous chances for intruders to infiltrate and compromise them. This current continuing challenge demands the need for more efficient and effective methods to shield Android users by recognizing various malwares. Though researchers and companies strive for a steady and consistent approach to increase the classification algorithms accuracy to categorize malicious Android applications, the search for the ultimate answer continues. Hence, we introduce an active learning-based classifier to advance the accuracy of classification in Android mal-ware. In our proposed system, we find the prediction probability of each test instance. If this probability falls below a certain threshold, we generate a similar test instance, incorporate it into the original dataset and retrain the model to get better accuracy of the classification. We evaluated the proposed system on CIC-Maldroid2020 dataset which comprises of 17,341 instances with five various android application categories consisting of Adware, Banking, SMS, Riskware, and Benign. This experiment results imply that the XGBoost (eXtreme Gradient Boosting) algorithm outperforms the various other five machine learning algorithms. Further, the active learning component of our proposed system improves accuracy from 95.34% to 98.5%. © 2024 IEEE.

关键词： Active learning

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An Integrated Mobile Applications for Enhancing Women's Safety - A Comprehensive Approach 2

An Integrated Mobile Applications for Enhancing Women's Safe...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Ashwini, K. Perla, Sree Neha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383287

Women's safety is a critical concern in today's society, necessitating innovative solutions that leverage technological advancements. This Proposed paper proposes two different applications, namely Womenify for the user especially women and Wopo for police which is combined with computer-aided dispatch (CAD) software to enhance women's safety and improve police response time. This research paper lays the groundwork for a comprehensive technical solution and presents valuable insights for creating safer communities for women. The proposed Womenify application will enable users to scan their surroundings using GPS tracking, access crime data in required areas to know whether the place is safe or not, and can also trigger emergency alerts. On the police side, the Wopo application provides patrolling schedules and real-time notifications. Further, the developed app can also be integrated with CAD software, using real-time data, ensuring easy accessibility for illiterate or unemployed women, expanding features, incorporating advanced technologies like predictive policing, and collaborating with law enforcement agencies. The effectiveness and functionality of the proposed application are evaluated through a feedback-based evaluation process which yielded positive results, indicating their effectiveness in enhancing women's safety. © 2024 IEEE.

关键词： Law enforcement

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Smart Traffic Flow Optimization with IoT and Machine Learning 15

Smart Traffic Flow Optimization with IoT and Machine Learnin...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Poojitha, D. Darak, Tanmay Samaddar, Ushnish Khanna, Manju Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Traffic signal optimization is crucial for the effective traffic management within cities with the primary goals being the reduction of bottleneck occurrences, avoidance of the extension of travelling time, and improvement of overall city street network function. Historically used approaches can hardly be adjusted to fluctuating traffic situation. Hence, a new methodology involving the use of machine learning-based traffic signal control with the assistance of the IR (Infrared) /UV (Ultraviolet) sensors for traffic flow sensing in real-Time environment is introduced in this study. The UV sensors make congestion distance measurements in real time which are assisted by IR sensors for traffic information acquisition. Historic data with data gathered from the traffic sensors are used to train the machine learning model to predict traffic. The strategy employs the use of heuristic search methodologies in the determination of signal timings with reference to congestion information that has been forecasted as well as congestion information that is being experienced. This paper discusses the fact that machine learning-based approaches, supported by sensor technology, can be used for effective traffic management systems that modify with the help of data. The success of this traffic management system is proved by its ability to minimize the MSE (Mean Squared Error) of congestion predictions to an average of 0.69. © 2024 IEEE.

关键词： Information management

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Consumer Behaviour Analysis for Customer Segmentation and Purchase Prediction 10

Consumer Behaviour Analysis for Customer Segmentation and Pu...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Gopakumar, Amritha Shine, Aathira Ajim, Amina Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350384369

E-commerce, short for electronic commerce, is the practice of placing orders online or negotiating a sale's terms and price. Although e-commerce is often associated with selling products and services to consumers, business-to-business (B2B) transactions also make use of it. To market their goods, many smaller consumer enterprises rely only on online sales. Nowadays, people are choosing to make purchases online as online transaction mechanisms and shopping platforms develop. Nevertheless, given that both clients and Merchants are unable to interact with clients face-to-face, there is a limited understanding of consumer demands. Due to this, businesses may struggle to gauge nuanced cues such as body language and personal preferences. Limited direct engagement can hinder the ability to address specific customer needs and preferences, potentially resulting in misinterpretation or overlooking crucial details. In turn, businesses may face difficulties in tailoring their products or services to meet the nuanced expectations of their customers, emphasizing the importance of alternative strategies such as detailed customer feedback mechanisms and data analytics to bridge this gap. The online system logs user activity and gathers data on consumer behaviour, allowing for the prediction of consumer purchasing patterns. Therefore, to improve the shopping experience of people, our study aims to use historical sales data to analyze consumer behaviour and do customer segmentation using RFM (Recency, Frequency, and Monetary) based K-means clustering and purchase prediction using various ML models to improve sales, out of which the combination of Logistic Regression and XGBoost performed the best. © 2024 IEEE.

关键词： Sales

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An Explainable AI Approach to Classify Smoker and Drinker Profiles via Medical Parameters 9

An Explainable AI Approach to Classify Smoker and Drinker Pr...

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

作者： Nanda, Krishna Maurya, Sakshi Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Smoking and consumption of excess alcohol is one of the major rising concerns in our society. In accordance with a WHO report released on July 31, 2023, tobacco use kills over 8 million people annually, of which 1.3 million are non-smokers who are exposed to passive smoking, and three million die from alcohol abuse. It not only affects the physical and mental health of the people who practices it but also affects the people who are around them. This work will play an important role as it helps in identifying these unhealthy habits from medical parameters which can contribute in prevention of the health risks which lie ahead. The results obtained are beneficial for the intended users like health professionals, insurance policy handlers and public health agencies where they will be able to identify the people who show addiction to these habits. We have performed various machine learning classification models which includes Light Gradient Boosting (LGB), Logistic Regression, Extreme Gradient Boosting (XGB), Random Forest, K-Nearest Neighbor etc, on the dataset which contains medical parameters as features which is used to categorize a person as a smoker or not a smoker or someone who used to smoke, and a drinker or not. Also, the explainability tools used helps to understand the key features which contribute to the decision making in the classification © 2024 IEEE.

关键词： Classification (of information)

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Banana Freshness Classification: A Deep Learning Approach with VGG16 6th

Banana Freshness Classification: A Deep Learning Approach wi...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Patre, Falguni Vasant Arya, Aditya Saranya, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819774227

In the food sector, ensuring the safety and quality of banana products is crucial. The classification of bananas into "fresh banana" and "rotten banana" categories is the main objective of this study. The studied banana varieties are cavendish, lady fingers, and red bananas. We use the VGG16 deep convolutional neural network with a dataset of high-resolution banana images. Our method includes training-testing ratios, picture enhancement, and rigorous data preprocessing. The results indicate how well the VGG16 model performs in classifying freshness, with good recall, accuracy, precision, and F1-score. Additionally, the model successfully differentiates between cavendish, lady finger, and red bananas, highlighting its capacity to handle minute variations. This work expands the application of image classification to other fruit varieties by offering a dependable technique for quality control and automated evaluation of banana freshness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Food industry

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Graph-Based Approaches to Detect Network Anomalies and to Predict Attack Spread 15

Graph-Based Approaches to Detect Network Anomalies and to Pr...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rithuh Subhakkrith, S. Kumar, Y. Jagadish Harish, R. Dheeraj, B. Vishnu Vidhya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Real world applications of data structures is enormous. Queues, stacks, trees, and graphs are commonly applied and implemented across various fields. To improve the efficiency of such implementations, optimisation through the underlying data structure can be considered. computer networks inherently are based on graphs with communicating systems as nodes and communication channels as edges. Information flows between the communicating systems through the communication channel. To detect suspicious connections and information flow, an optimal graph structure is implemented by modelling the information flow as edges, and retaining the communicating systems as nodes. Graph analytics is performed to capture graphical relationships, and the final graph structure is formed with the features of the information flow split into node features and edge features. Then this graph structure is passed to Graph Neural Networks (GNN) that captures hidden relationships of the graph, and transforms these graph features in such a way that basic Machine Learning models like k-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest can train themselves and model into better anomalous connection classifiers. Eleven baseline models are trained using the traditional way, and using GNN transformed embeddings, and their performances are observed. Testing shows that all models show an improvement in the evaluatory scores. Finally, a logic is provided to build an attack sequence graph to track the route in which the attack spreads among infected systems. © 2024 IEEE.

关键词： Attack Sequence Graph Edge Classification Graph Analytics Graph Neural Networks Network Security

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Energy Conservation Using Piezo Electric Effect Through Modern Technology 9

Energy Conservation Using Piezo Electric Effect Through Mode...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Durga, Kanaparthi Dhathri, Kammarapalli Sri, Kanchukommula Sai Lavanya, Kandagaddala Naga Lakshmi School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350377972

This paper seeks to establish the energy conservation by using piezo-electric effect through some Modern Technology's and the project aims to investigate the application of modern technology in various fields to reduce energy consumption and increase energy efficiency. We focus on three key areas: buildings, transportation, and industry. In each of these areas, we identify the most significant energy consumption sources and potential opportunities for energy savings using modern technology. This aims to explore innovative solutions for sustainable energy generation by harnessing mechanical energy from human movement. This research focuses on integrating piezoelectric cells into everyday footwear and flooring tiles to convert mechanical stress into electrical energy. The project involves designing and fabricating prototypes of energy-harvesting shoes and tiles. The efficiency of energy conversion, the durability of the materials, and the overall feasibility of integrating such systems into daily life will be evaluated. The study will also address the scalability of this technology and its potential impact on reducing the reliance on traditional power sources. © 2024 IEEE.

关键词： Piezoelectricity

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Design and Implementation of the Dynamic Source Routing Protocol with Packet Numbering for Efficient Route Discovery, Improved Packet Delivery and Routing Optimization in Dynamic Network Scenarios 13

Design and Implementation of the Dynamic Source Routing Prot...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Kannan, Srinidhi Suryamritha, M. Balaji, Varshini Rajesh, M. Sreevidya, B. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350305463

Dynamic Source Routing (DSR) protocol is mostly used in the wireless mesh network, where all the nodes are in a mesh topology, it is also used in Mobile ad-hoc Networks (MANETs). As it is in mesh topology that the communication can be directly established between the nodes or utilizing intermediate nodes. The proposed work mainly focuses on the necessary modifications to be made in the DSR protocol that is to add extra information to the packet format - a Packet Number, which is assigned when the packet is sent. This is verified by simulating a wireless network using network simulator NS2. © 2024 IEEE.

关键词： Mobile ad hoc networks

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A Graph-Based Approach for Estimating Object Interactions in Surveillance Video Synopsis 2

A Graph-Based Approach for Estimating Object Interactions in...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Sood, Varnika Ray, Santosh Kumar Mahato, Kiran Kumar Sidharth, A.V. Namitha, Kalakunnath Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350328202

Video synopsis is a technique that condenses surveillance videos into compact equivalent videos. The objective of this paper is to identify and group related moving objects (also referred to as tubes) in a video using a graph-based video synopsis method. To do this, we first develop an interaction matrix to determine the degree of pairwise interaction between the tubes. A tube-set generation graph is then suggested as a way to combine linked tubes into a single tube set. Publicly available video datasets have been utilized by us to demonstrate the effectiveness of our method. Our technique has come up with another way of video synopsis;that is, focusing on the context of the original movie and picking out important details. This makes it a reliable surveillance approach as it greatly improves security efficiency through fast and accurate compression of videos. Moreover, our method grounded in graph preservation eases our understanding of what connects various elements in a film or video. © 2024 IEEE.

关键词： Graphic methods

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