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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Raja, M. Manicka Kumar, G. Mokesh Anand Sanchita, T. Sandhya, A. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems TN Coimbatore India

ISBN: (纸本)9781665474511

Artificial Intelligence is growing to be the future of today's world. It builds a path for communicable applications and an easier understanding for the system and the user. By adopting the advantages of artificial intelligence, our project focuses on estimating and delivering the right resources for a college student. The web app, Edu. Social is created to determine and seek deep skills of a student and provide them with the right resources to heighten their knowledge. The web app would analyze the right resources based on the initial and prime inputs given by the students. The resources comprise placement opportunities, certification suggestions, workshops, seminars, activities and many more that are happening around the respective student. The web app does not just focus on the academic perspective but also the inner passion of a student. Therefore, this platform would be a great benefit for a student to gain experience and to see the viable resources put forth according to their field of interest and choice. The main focus of this Web Application is to keep students up to date by notifying them about new technologies available and where to focus on by providing frequent assessments to know where they stand. © 2023 IEEE.

关键词： Students

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Sentimental Analysis on Amazon Camera Reviews using Naive Bayes Algorithm

Sentimental Analysis on Amazon Camera Reviews using Naive Ba...

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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Santhosh, R. Vignesh, S.V. Rithish, E. Mahendhiran, P.D. Sri Krishna College of Engineering and Technology Computer Science and Business Systems Tamil Nadu Coimbatore India

ISBN: (纸本)9798350333602

All human endeavors revolve around opinions, which also significantly affect human behavior. Sentimental analysis with opinion mining research focuses attitudes & feelings. There are several difficulties in opinion mining. The fact that a term is either good or negative based on the situation is the first difficulty. People's differences in viewpoint expression present a second obstacle. The usage of online social media facilitates decision-making with feedback. Consequently, social media communications may be used for sentiment analysis. Dealing with public health concerns is the goal of the suggested endeavor. Traditional surveillance methods are costly, have a narrow field of view, and take a long time to collect data on public health issues. Social media communications, which are available for free and are created globally, are employed to solve this issue. A two-step word alignment technique can be used to gauge public concern in this. Personal and news reviews make up the first two categories of raw reviews. Reviews are further separated into personal negative and non-negative categories at second stage. In both phases, the dataset trained may assessed using a ML model like Naive Bayes, and training data is generated automatically using emotion-focused, clue-based method. The suggested approach has increased the field of epidemics' accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends 2

Evaluation of AI Techniques for Detecting Deceptive Reviews ...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Samineni, Lavanya Peddi, Anudeep Kasukurthi, Aravindha Rao, M.V.P. Chandra Sekhara Niharika, Gokavarapu Chereddy, Sai Vignesh Rvr and Jc College of Engineering Department of Computer Science and Business Systems Guntur India Guntur India

ISBN: (纸本)9798350346640

With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.

关键词： Support vector machines

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Food Volume Prediction and Classification using Multi-Task Learning 6

Food Volume Prediction and Classification using Multi-Task L...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Sari, Yuita Arum Nakazawa, Atsushi Gofuku, Akio Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Japan Brawijaya University Faculty of Computer Science Malang Indonesia

ISBN: (纸本)9798350374162

Food volume prediction is critical for calculating calories in food analysis and nutritional evaluation. However, obtaining data and measuring volume from the 3D sensor camera concurrently is time-consuming. Additionally, high computing power is necessary to compute the volume. Therefore, machine learning techniques are employed to extract specific data collected from the data collection process for learning purposes to save time. In this study, we employ Multi-Task Learning to predict volume and categorize food due to their interdependence simultaneously. We evaluate several backbones and find that ResNet50 attains the best performance with an MAE of 4.87 for volume prediction and 100% food classification accuracy. Our proposed method showcases outstanding results, aided by an efficient volume measurement methodology © 2024 IEEE.

关键词： food volume prediction machine learning multi-task learning

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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Enhancing resilience in complex energy systems through real-time anomaly detection: a systematic literature review

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Energy Informatics 2024年第1期7卷 96页

作者： Aghazadeh Ardebili, Ali Hasidi, Oussama Bendaouia, Ahmed Khalil, Adem Khalil, Sabri Luceri, Dalila Longo, Antonella Abdelwahed, El Hassan Qassimi, Sara Ficarella, Antonio Department of Engineering for Innovation University of Salento Lecce Italy Bologna Italy Computer Science Department Faculty of Sciences Semlalia Cadi Ayyad University Marrakesh Morocco Computer Science Department Faculty of Science and Technology Cadi Ayyad University Marrakesh Morocco Department of Research and Development HSPI SpA-Roma Rome Italy Department of Embedded Systems and Artificial Intelligence MAScIR UM6P Rabat Morocco

As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems. © The Author(s) 2024.

关键词： Cyber attacks

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Smart Contracts for SMEs and Large Companies 2

Smart Contracts for SMEs and Large Companies

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Liu, Christian Gang Bodorik, Peter Jutla, Dawn Dalhousie University Faculty of Computer Science Halifax Canada Sobey School of Business Saint Mary's University Halifax Canada

ISBN: (纸本)9798331530099

Research on blockchains addresses multiple issues, with one being writing smart contracts. In our previous research we described methodology and a tool to generate, in automated fashion, smart contracts from BPMN models. The generated smart contracts provide support for multi-step transactions that facilitate repair/upgrade of smart contracts. In this paper we show how the approach is used to support collaborations via smart contracts for companies ranging from SMEs with little IT capabilities to companies with IT using blockchain smart contracts. Furthermore, we also show how the approach is used for certain applications to generate smart contracts by a BPMN modeler who does not need any knowledge of blockchain technology or smart contract development - thus we are hoping to facilitate democratization of smart contracts and blockchain technology. © 2024 IEEE.

关键词： Automated Generation of Smart Contracts from BPMN Models Blockchain Smart Contracts SMEs Trade of goods and services

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Arduino-Based Gas Leakage Monitoring and Treatment System 1

Arduino-Based Gas Leakage Monitoring and Treatment System

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Muhammad, Faiq Rafi Putra, Alfa Satya Aribowo, Arnold Pelita Harapan University Faculty Of Computer Science Computer Systems Department Tangerang Indonesia Pelita Harapan University Faculty Of Computer Science Information Systems Department Tangerang Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The use of gas in daily life is very important, especially in household life where it has become a major need. There are risks related to gas usage to the user and the community, such as gas leakage, which can be fatal and can cause fire to break out. Therefore, a system is needed to monitor and safely handle gas leakage. This paper discusses a gas leak monitoring and treatment system that uses gas sensor to detect gas concentration level, solenoid valve to open and close the gas valve, LCD to monitor and display gas concentration level, an exhaust fan to dispose gas from the room, buzzer as warning alarm, push-button switch to manually open gas valve after it has been closed, and Cayenne platform to send email notification to the user when gas leakage is detected. These devices are controlled by an Arduino Mega microcontroller board. From the system design and testing results, it can be concluded that the Arduino-based gas leakage monitoring and treatment system is working as expected in safely handling gas leakages. The success rate of gas sensor, solenoid valve, buzzer, push-button, and exhaust fan testing is 100%, and the success rate of email notification testing is 80%. © 2022 IEEE.

关键词： Solenoid valves

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Malware Detection Based on Stack Ensemble Using Machine Learning 2

Malware Detection Based on Stack Ensemble Using Machine Lear...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Eke, Christopher Ifeanyi Olamide, Fawaz Ibrahim Phiri, Musa Mwenge, Mulenga Faith Fatokun, B. Shuib, Liyana Federal University of Lafia Faculty of Computing Department of Computer Science Nasarawa State Nigeria Mulungushi University Department of Computer Science and Information Technology Kabwe Zambia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia University of Malaya Faculty of Computer Science & It Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798331542573

Information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted at these devices are increasing at a rapid pace. Among these types of attacks on information systems is malicious software also known as malware. A cyber-attack based on malware has the potential to cause loss or manipulation of confidential information. Currently, malware creators who use polymorphic techniques to evade detection can bypass traditional signature-based methods of detecting malware. Thus, various researchers have turned to Machine Learning (ML) techniques to enhance the detection of malware. However, despite the promising results, ML-based classification of malware remains a challenging task that the research community is yet to solve. Therefore, in this study, we propose an ML-based malware detection system that incorporates the AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors Algorithm (KNN) algorithms into an ensemble stack with gradient boost as the ensemble boost for the stack meta estimator. We also use Extra Trees Classifier for feature selection. Multiple performance measures are utilized to assess the system's performance while using the Kaggle Malware Dataset. Our findings demonstrate that the proposed method successfully detects malware samples with remarkable accuracy, precision, recall, F1, and area under the curve (AUC) scores of 99.4%, 99.3%, 99.4%, 99.4%, and 99.6%, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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AI in Sustainability:Assessing its Functions and Environmental Impact 10

AI in Sustainability:Assessing its Functions and Environment...

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10th IEEE Smart World Congress, SWC 2024

作者： Alhasan, Khawla Chen, Liming The Arab Open University Faculty of Business Studies Kuwait Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798331520861

Artificial Intelligence (AI) has fundamentally transformed various industries, including healthcare, transportation, agriculture, energy, and media. However, while AI's impact is widely recognized, its sustainability remains a critical concern due to significant computational and environmental costs, as well as ethical issues such as biased systems and misinformation. This paper explores the dual dimensions of AI's impact on sustainability: its potential to promote sustainability across sectors and the sustainability of AI systems themselves. Using a comprehensive review of existing literature, we address both the benefits and challenges associated with AI in achieving sustainable goals and the need for a more integrated approach to understanding its environmental, social, and economic impacts. Our analysis of the 'AI Global Index' dataset, which evaluates AI capabilities across 62 countries, highlights key trends and disparities in AI development. The findings underscore the need for a holistic approach to AI sustainability that encompasses its full range of effects and interactions with various dimensions of sustainability. © 2024 IEEE.

关键词： Sustainable agriculture

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