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

作者： Hussain, Syed Mohideen, S. Pakkir Institute of Science and Technology B.S Abdur Rahman Crescent Computer Science and Engineering Department Chennai India Institute of Science and Technology B.S Abdur Rahman Crescent Computer Application Department Chennai India

ISBN: (纸本)9798350346640

Framework to avoid global crime especially terrorist attacks, bomb blasts, and drone attacks all major and minor attacks with the help of SCMD (Suspicious Cryptographic Message Detection) such as cryptanalysis of SMS or any messaging system. When the server received a suspicious encrypted message, the proposed framework will decrypt the suspicious message and facilitate in predicting the crime, criminal name, location, and criminal details and report to the electronic crime agency. To date, no proper solution to avoid such a big crime as the world is facing problems still today along with this machine learning technique, Artificial Intelligence and semantic web ontology and Cryptanalysis of Enigma Cipher. Terrorists communicate with their teammates in different locations of the world and instruct with resources to attack the government or public while communicating they use the encrypted message to maintain secrecy. Our framework at the server site will detect encrypted messages and decrypt them with the help of Cryptanalysis of Enigma Cipher techniques. The framework will decrypt a suspicious message and expedites predicting the type of crime from microblogs before it gets executed by criminals. Details of criminals will be alerted to cybercrime the department that reduces the tension for the various security departments. Criminals use an encrypted message to pass activity among teammates who may be in living in any corner of the World and other people may decrypt and convert it to plain text and takes action. © 2023 IEEE.

关键词： Cryptography

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Play Store App Analysis & Rating Prediction Using Classical ML Models & Artificial Neural Network 7

Play Store App Analysis & Rating Prediction Using Classical ...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Moharana, Bhimasen Biswal, Bhramara Bar Dey, Snehasis Rath, Manas Kumar Banerjee, Shobhan Lovely Professional University Department of Computer Science and Engineering Punjab India College of Engineering P.G. Department of Computer Science & Applications Bhubaneswar India College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application Baripada India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350304268

Each app available in the App Store or Play Store has various aspects linked to it such as its version, category, number of installations, genre, etc. which makes it unique, robust & popular. The reviews and ratings given by end users play an important role for both the developers and other users for the performance and survival of the app in the marketplace. Reviews are just a description for justification of the rating given by a user & the rating plays an important role in the first look. In this paper, we have used the Play Store Analysis Dataset to descriptively analyze the various attributes present in it and create models that predict the rating of an app, given its specifications. The models have been compared based on a common accuracy metric based on which their reliability can be judged and used in real-time rating predictions for a new application in the store for which a certain amount of data is available over some period. © 2023 IEEE.

关键词： Descriptive Statistics ML Classification Models Model Comparison Play Store App Analysis Rating Prediction

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Determining human-coronavirus protein-protein interaction using machine intelligence

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Medicine in Novel Technology and Devices 2023年第2期18卷 123-142页

作者： Arijit Chakraborty Sajal Mitra Mainak Bhattacharjee Debashis De Anindya J.Pal Bachelor of Computer Application Department The Heritage AcademyKolkataIndia Department of Computer Science and Engineering Heritage Institute of TechnologyKolkataIndia Department of Biotechnology Heritage Institute of TechnologyKolkataIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataIndia University of Burdwan BurdwanIndia

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities *** research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral ***,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical *** article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based *** classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this *** proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment ***,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

关键词： Coronavirus Ensemble learning Machine intelligence Protein-protein interaction

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CLAACS-IOD: Certificate-embedded lightweight authentication and access control scheme for Internet of Drones

CLAACS-IOD: Certificate-embedded lightweight authentication ...

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作者： Sadhukhan, Dipanwita Ray, Sangram Dasgupta, Mou Rodrigues, Joel J. P. C. Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla India Department of Computer Application National Institute of Technology Raipur Raipur India Qingdao China Instituto de Telecomunicações Covilhã Portugal

In recent era, the unmanned aerial vehicles (UAVs) commonly known as drones has emerged as a one of the most significant and promising tools which has demonstrated its wide range of implementations variating from commercial domain to the field of defense due to its distinct capabilities such as inspection, surveillance, precision and so forth. Internet connected drones provides a propitious trend that boosts the flying safety, and service qualities of the UAVs where numerous low-altitude drones winged in different flying regions for executing a precise task such as gathering the real-time information from the unuttered environment to be interpretated by users. Nevertheless, the open-access insecure communications in hostile environment, the issues like safety and confidentiality threats, various security concerns such as the leakage of flying courses, identities, position, and gathered data by the drones are upstretched. To address these security concerns, access control mechanism provides a potential service in terms of authentication and key agreement for securing the communication between the individual drones within their respective flying regions. This article introduces a robust, efficient, lightweight, and privacy preserving ECC integrated access control approach by employing digital certificate with considering the high dynamicity and mobility of the drones. The designing of this proposed approach, that is, CLAASC-IoD is influenced by the aim of inter-drone and drone-to-ground station communication in the IoD paradigm. The detailed analysis of security using probabilistic random oracle model as well as simulation using well-accepted security verification tool AVISPA and comparative performance evaluation supports the claim of robustness, effectiveness, and proficiency. © 2023 John Wiley & Sons Ltd.

关键词： Internet of things

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Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval 15

Interpreting Deep Convolutional Neural Networks for Medical ...

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

作者： Kasture, Shreyas Maurya, Sudhanshu Verma, Rohan Yadav, Ajay Kumar Shukla, Amit Nagpur Campus Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India United Institute of Management Department of Computer Application Prayagraj India Centre of Artificial Intelligence & Robotics Mandi India

ISBN: (纸本)9798350370249

The robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. This interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people's lives are at risk. The study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. The models for proposed models succeeded in the clinical tasks of medical diagnosis. They reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. These models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. The suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%. © 2024 IEEE.

关键词： Active Learning Deep Neural Networks Interpretation Synthesis Medical Diagnosis Network Traffic Forecasting Saliency Map Aggregation and Saliency Maps

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Improving Connectivity of IoT-Enabled Networks Through Big Data Analytics 15

Improving Connectivity of IoT-Enabled Networks Through Big D...

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

作者： Kiran, K.S. Sen, Sumanta Sharma, Sunil School of Engineering and Technology Karnataka Bangalore India ARKA JAIN University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

The net of things (IoT) has quick grow to be the backbone of many corporations, taking into account real-time statistics collection and automatic tasks. However, the connectivity of these IoT-enabled networks can become without difficulty congested when huge amounts of data are generated in a brief time frame. To be able to hold a high stage of overall performance, big facts analytics can be used to research the massive volumes of generated statistics, provide insights and locate anomalies to improve community connectivity. Massive statistics analytics will permit higher records processing and evaluation competencies, become aware of regions of improvement and optimize network operations. Additionally, predictive analytics techniques can be used to forecast upcoming community congestion and take movement that could enhance network performance in real time. This, but, requires green facts collection, garage and control gear to ensure that the big records analytics answer is effective and efficient. This paper investigates how massive records analytics may be used to improve the connectivity of IoT-enabled networks through leveraging the energy of predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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Classification of Foods based on Bijective Soft Set

Classification of Foods based on Bijective Soft Set

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International Conference on Computing and Networking Technology (ICCNT)

作者： Priyanka Das Rupam Bhattacharya Dept. of Computer Application and Science Institute of Engineering & Management Kolkata

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers.

关键词： Accuracy Neural networks Set theory Expert systems Recommender systems Guidelines

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Bioinspired Clustering in UWSNs Using Monarch Butterfly Optimization Compared with Ant Colony Optimization Algorithm

Bioinspired Clustering in UWSNs Using Monarch Butterfly Opti...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Indira, G. Rani, Ruchika Chiranjevi, D. Husain, Saif O. Yogi, Rahul Nandhini, T.J. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Aditya Engineering College Department of Computer Science & Engineering India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

The Monarch Butterfly Optimization (MBO) algorithm for clustering in Underwater Wireless Sensor Networks (UWSNs) is investigated and compared to the performance of the Ant Colony Optimization (ACO) algorithm in this paper. As is evident from the outcomes depicted in the above graphs MBO consumes an average of 31% less energy than the ACO while lasting longer in the network. 7J or make a network get 790 round life cycle while ACO did 34. 5J and 730 rounds. The results indicate that MBO is more advantageous to cluster in the UWSNs making it a viable solution to boost up the network productivity and the overall energy utilization efficiency. © 2024 IEEE.

关键词： Ant colony optimization

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Performance Analysis of Deep Learning Techniques for Time Series Forecasting

Performance Analysis of Deep Learning Techniques for Time Se...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Tripathy, Nrusingha Hota, Sarbeswara Prusty, Sashikanta Nayak, Subrat Kumar Siksha 'o' Anusandhan Deemed to Be University Department of Computer Science and Engineering Odisha Bhubaneswar India Siksha 'o' Anusandhan Deemed to Be University Department of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350339369

Time series data forecasting is a crucial topic in economics, business, and finance. New methods are being created to evaluate and predict time series data as a result of recent improvements in computing power and more significantly the development of sophisticated machine learning algorithms and methodologies, such as deep learning. This work boons a deep learning-based time series forecasting method and a comparative study among three models that are ARIMA, LSTM and FB-Prophet by using the current knowledge as time series then draws out the key elements of prior data to forecast the values of an upcoming time sequence. In this work, we have taken an electric production dataset, from which 70% of data used as training and 30% of data used as testing the methods. From the experimental result it is found that ARIMA model outperforms the other two model in forecasting this timeseries data. © 2023 IEEE.

关键词： Forecasting

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Intelligent Control of an Integrated Energy System: Low Carbon Modeling and Intelligent Optimization 6

Intelligent Control of an Integrated Energy System: Low Carb...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Fu, Zezheng Lin, Wenting Yang, Degang College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China

ISBN: (纸本)9798350353594

An integrated energy system is a complex system that requires intelligent control to optimize its operation. This paper proposes an intelligent control method for integrated energy systems based on a low carbon model. The model integrates a variety of low carbon technologies such as carbon capture systems, demand response, carbon emissions trading, and clean energy generation. The effect of the proposed intelligent control methods on reducing system costs and carbon emissions is verified through a case study. The results show that the combination of the intelligent control method and the low carbon model leads to a significant reduction in system operating costs and carbon emissions. This provides an important reference for intelligent control of the existing integrated energy system from a low-carbon approach. © 2024 IEEE.

关键词： Carbon capture and utilization

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