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Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

Intelligent Automation & Soft Computing 2023年第2期35卷 2337-2351页

作者： S.M.Udhaya Sankar Mary Subaja Christo P.S.Uma Priyadarsini Department of Information Technology Velammal Institute of TechnologyChennai601204India Department of Networking and Communication SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Energy conservation has become a signiﬁcant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain *** a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efﬁcient way remains a ***,the clustering strategy employs to enhance or extend the life cycle of *** identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the ***,the critical issue was determining the best SH for ensuring timely communication *** secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize *** sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data *** modeled our methods to accom-plish power-efﬁcient multi-hop ***,protected navigation helps to preserve energy when *** suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

关键词： Wireless sensor network wireless security wireless routing clustering ad hoc network

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Smart Agricultural Greenhouse System: A Context-Aware Application

Smart Agricultural Greenhouse System: A Context-Aware Applic...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Swain, Sujata Niyogi, Rajdeep School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science IIT Roorkee Roorkee India

ISBN: (纸本)9783031513374

Smart applications are getting more powerful and cheaper cost due to the advancement in sensor technology. In this chapter, we have considered a smart greenhouse application. The important parameters of the smart greenhouse are to control light, temperature, humidity, and water. Greenhouse can provide a favourable growth environment for plants. This smart greenhouse system is a context-aware application. When the context changes in the environment, the application automatically adapts it and does the changes seamlessly without user intervention. In this chapter, we propose a model for the smart greenhouse system. The proposed model can adapt to context changes such as temperature rises, and it automatically turns on the AC seamlessly. The proposed model is based on the machine learning approach. The model maintains the greenhouse temperature, humidity, and sunlight duration in the suitable ranges of the plant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context Greenhouse Machine learning NLP Sensors Web service

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Brain MRI Image Analysis for Alzheimer’s Disease (AD) Prediction Using Deep Learning Approaches

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SN computer science 2024年第1期5卷 160页

作者： Singh, Archana Kumar, Rakesh Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur 273010 India

Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative condition that causes memory impairment decline. Alzheimer’s disease (AD) stands as one of the most pervasive chronic ailments affecting the elderly population, presenting a considerably high prevalence rate. The significance of early detection in the treatment of AD cannot be overstated, owing to the potentially severe cognitive and neurological impairments that emerge as the disease progresses. Swift and accurate diagnosis holds a pivotal role in curbing the extent of brain deterioration that becomes more pronounced in the later stages of the disease’s course. Many strategies have been used to determine the pattern of disease to detect Alzheimer’s disease. The similarity of brain patterns in older people and different stages of Alzheimer’s disease makes categorization difficult. In recent times, the domain of medical imaging has witnessed a surge in the prominence and accomplishments of Deep Learning (DL) and Machine Learning (ML) techniques. These advancements have not only taken center stage in the evaluation of medical images but have also ignited substantial enthusiasm for enhancing the diagnosis of Alzheimer’s disease. Within this landscape, Deep Learning and machine models have risen to prominence, outpacing conventional Machine Learning methods in precision and efficiency, particularly in the realm of Alzheimer’s disease detection. A pivotal approach that has gained traction involves the utilization of pre-trained Convolutional Neural Network (CNN) models. These models, primed with extensive prior learning, exhibit an enhanced ability to categorize various stages of Alzheimer’s disease. Through the adept utilization of Deep Learning models, the categorization of six distinct phases of Alzheimer’s disease becomes attainable, offering a promising avenue for refining diagnostic accuracy and comprehension. Normal control (NC), Significant memory concern (SMC), Early mild cognitive impair

关键词： Alzheimer’s disease CNN pre-trained models MRI MRI pre-processing

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Trustworthy Artificial Intelligence and Automatic Morse Code Based Communication Recognition with Eye Tracking

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Journal of Mobile Multimedia 2023年第6期19卷 1439-1461页

作者： Medichalam, Krishnakanth Vijayarajan, V. Kumar, V. Vinoth Iyer, I. Manimozhi Vanukuri, Yaswanth Kumar Prasath, V.B. Surya Swapna, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore 632014 India Department of Computer Science and Engineering East Point College of Engineering and Technology India Department of Electrical Engineering and Computer Science University of Cincinnati 45221 OH United States Department of ECE Dr MGR Educational and Research Institute Chennai 95 India

Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a computer vision based approach that automatically characterizes the characters conveyed wherein a person can communicate to system or another person through Morse code with eye gestures. We can decode this visual eye tracking based language with the help of our automatic computer vision driven method. Our approach uses a normal webcam to detect the gestures made by the eyes and are interpreted as dots and dashes. These dots and dashes are used to represent the Morse code-based words. Image processing techniques-based blink and pupil detectors are employed. Blink detector helps us to detect a blink and the time that took for each blink. A blink that takes 2 to 4 seconds is acknowledged as a dot whereas a blink that takes more than 4 seconds is represented as a dash. The pupil detector helps us to detect the movement of the pupils, and if pupils move towards right with respect to a person then it is acknowledged as next letter and if the pupils are moved towards left with respect to a person then it is acknowledged as next word. In this way, we decode the Morse code which will be communicated using eyes and establish a non-detectable communication between a person and an automatic system. Our experimental results on an unconstrained visual scene with preliminary greeting words indicate the promise of an automatic eye tracking based system with success rate of 98.25% that can be of use in non-verbal communications. © 2023 River Publishers.

关键词： blinks communication eye detection face detection gestures image processing Morse code

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Integration of AI in the Realm of Software Development

Integration of AI in the Realm of Software Development

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Bhuvana, J. Ranjan, Vivek Bhaduariya, Nirmednra Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

Software engineering has increasingly utilized "Artificial Intelligence"(AI) to improve self-organizing IT solutions. This research aims to fill the existing void in the thorough examination of artificial intelligence's (AI) impact on software engineering. To achieve this, the study used a combination of an extensive literature review and qualitative Conversations with software engineers. The objective of the study is to evaluate the present status of AI integration, possible progress, and the accompanying hazards in the realm of software development. The results demonstrate that artificial intelligence has made substantial progress in multiple domains. By employing algorithmic methodologies, it has automated time-consuming chores such as debugging and documentation, resulting in a more efficient software development and testing process. AI has facilitated the methodical examination of extensive datasets, detecting patterns and novel clusters of information. Moreover, the utilization of neural networks has enabled comprehensive analysis of these datasets. Hence, AI expedites progress, diminishes development expenses, and augments total efficacy in software engineering. © 2023 IEEE.

关键词： AI DL ML SDLC and pattern recognition

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Neural Network-based Approach to Predict Protein Secondary Structure 2

Neural Network-based Approach to Predict Protein Secondary S...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Rahman, Arifur Mahmud, Anik Chandra Shill, Pintu Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9781665456302

Protein Secondary structure prediction is an emerging topic in bioinformatics to understand briefly the functions of protein and their role in drug invention, medicine and biology. In our research we have applied two recurrent neural network based approach Bi-LSTM (Bidirectional Long Short-Term Memory) and LSTM (Long Short-Term Memory). Our research was focused on primary structure up to 134 in length of amino acids. Initially our proposed model produced a 'Indexed Lexicon of corpus' using tri-gram conversion for primary structure strings. Each primary structure tri-gram transformed snippets is substituted with its associated index mentioned in 'Indexed corpus'. The indexed parameter vector inputted into our proposed Bi-LSTM and LSTM model. We got best accuracy when we have used two Bi-LSTM and three LSTM layers respectively in Bi-LSTM and LSTM models. To prevent biasness and minimize overfitting problem we have utilized two dropout layers for each of Bi-LSTM and LSTM model. We have operated our model on ccPDB 2.0 benchmark dataset. There is total eight states protein secondary structure in this dataset. For this sst8 secondary structure we have achieved 83.24% accuracy for our proposed LSTM model and 89.10% accuracy for our Bi-LSTM model. We have configured our model to run for 50 epochs with batch size 64. For compilation of our models we have utilized 'adam' optimizer and the 'categorical crossentropy' loss function. To make dataset balanced to our model we have also employed 5-fold cross validation. © 2023 IEEE.

关键词： Long short-term memory

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Efficient Cooling System of Cloud Data Center by Reducing Energy Consumption 6th

Efficient Cooling System of Cloud Data Center by Reducing En...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Natasha, Nazia Tabassum Fakir, Imran Falguni, Sabiha Afsana Mim, Faria Tabassum Fahim, Syed Ridwan Ahmed Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031503290

Cloud computing makes computers a utility and allows scientific, consumer, and corporate applications. This implementation raises energy, CO2, and economic problems. Cloud computing companies are concerned about energy use in cloud data centers. Green Cloud Environments, known as GCE, have provided formulations, solutions, and models to reduce the environmental effect as well as energy consumption under the latest models while considering components for static and dynamic clouds. Our technique models cloud computing data centers. To accomplish this, you must understand trends in cloud energy usage. We analyze energy consumption trends and show that by using appropriate optimization techniques guided by our energy consumption models, cloud data centers may save 20% of energy. Our study is incorporated into cloud computing while monitoring energy usage and helping to optimize on a system level. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy efficiency

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Prediction of Pneumonia from 3D CT Images Employing Deep Neural Network based Feature Extraction Method 26

Prediction of Pneumonia from 3D CT Images Employing Deep Neu...

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

作者： Sima, Tahera Khatun Islam, Rafiqul Dhaka University of Engineering & Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350359015

Pneumonia is an inflammatory lung infection that can cause life-threatening consequences such as difficulty breathing, fever, and chest pain. Early detection enables timely action, which reduces the severity of the ailment and improves patient outcomes. However, because of the complexity and diversity of its indications, pneumonia prediction poses considerable problems, making accurate diagnosis and prognosis difficult. In this work, a novel 3D feature extraction method for pneumonia prediction from computed tomography (CT) image volume is proposed. A 3D convolution neural network with rigorous pre-processing processes is used to construct the feature extraction method. The obtained features are then utilized for making decisions in which a number of layers are put together to develop the pneumonia prediction model. A 3D CT image dataset with 160 volumes and 40 volumes for training and testing respectively were used for the experimental evaluation. Several performance indicators were employed to evaluate performance, and cutting-edge methods were utilized to compare results. From the experiment, the proposed method achieved 95% training and 85% validation accuracy in which the state of art 3D CNN models accuracy reached up to 88-92% and 76-80% respectively. In conclusion, it was shown that the suggested algorithm outperforms others in terms of predicting pneumonia from 3D CT volume. © 2023 IEEE.

关键词： classification Convolution neural network CT imaging Feature extraction Pneumonia

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Detecting Disease In Livestock and Recommending Medicines using Machine Learning 2

Detecting Disease In Livestock and Recommending Medicines us...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Lavanya, V. Kannan, A.Dinesh Kamaleshwaran, S. Nishanth, R. Kamatchi, S.Muthu Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350347579

Livestock is one of the critical socioeconomic assets in developing countries like India. However, the lack of a reliable and timely diagnosis system for identifying livestock diseases has led to significant losses in the livestock population, hindering efforts to achieve food security and reduce poverty in the country. To address this issue, a study proposed the integration of an expert system with machine learning and image processing. According to the 2019 Livestock Census, India has a total livestock population of around 535 million, which includes cattle, buffalo, sheep, goats, and pigs. The livestock sector is an important source of income for millions of households in India. As per the 2019 Livestock Census, there are around 145 million households involved in livestock farming and related activities in the country. The livestock sector contributes significantly to the Indian economy. In 2020, the total value of livestock output in India, excluding the value of horses, ponies, mules, donkeys, camels, and yak, was estimated to be around 9.37 trillion Indian rupees (around 125 billion US dollars), which accounts for around 4.2% of country's GDP. The dairy sector is a major contributor to the livestock economy in India. It accounts for around 70% of the total value of livestock output in the country. In addition to dairy, the livestock sector also provides meat, wool, leather, and other products. According to the latest available data from the Ministry of Statistics and Program Implementation, the contribution of the livestock sector to India's Gross Value Added (GVA) in 2020-21 was 5.04%. Since GVA is more accurate measure of the sector's contribution to the economy than GDP, it is concluded that livestock sector contributed around 5% to India's economy in 2020-21. According to a study by the National Dairy Development Board (NDDB) of India, there is an average of one veterinarian for every 5,000 cattle in India. This study improves the accuracy of livestock dis

关键词： Mammals

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Adaptive Gamma Correction with Unsharp Masking and Gaussian filter for Image Contrast Enhancement

Adaptive Gamma Correction with Unsharp Masking and Gaussian ...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Mishra, Pawan Kumar Patni, Jagdish Chandra Saini, Shivika Kishore, Jaydeep Baloni, Dev Verma, Shalini Graphic Era Deemed to Be University Computer Science & Engineering Dehradun India Nagpur Campus Pune India Uttarakhand Technical University Department of Computer Science Dehradun India Manipal University School of Computer Science & Engineering Department of Aiml Jaipur India Quantum University Quantum School of Technology Roorkee India Doon Institute of Engineering & Technology Computer Science & Engineering Rishikesh India

ISBN: (纸本)9798350382723

In this research work image processing is done for direct application of digital images. Image processing is used for the enhancement of process includes with it. Adaptive histogram equalization is an appropriate method for improving the quality of images. This technique is used with gamma correction. Unsharp masking and Gaussian smoothing filters are also applied to improve the visibility as well as to remove the harshness of images. The comparative results of different approaches are also shown in this research work. The proposed method is superior to earlier developed methods by testing on various parameters such as MSE, PSNR, MAE, and AMBE. The suggested strategy maintains brightness in different types of images. © 2024 IEEE.

关键词： Image enhancement

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