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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Saidala, Ravi Kumar Ramnath, M. Komala, C.R. Vidhya, N.G. Taqui, Syed Noeman Rajendiran, M. Alliance University Department of Computer Science And Engineering Karnataka Bangalore560076 India Ramco Institute of Technology Department of Artificial Intelligence And Data Science Tamil Nadu Rajapalayam626117 India Hkbk College of Engineering Department of Information Science And Engineering Bengaluru560 045 India St. Joseph's Institute of Technology Omr Department of Information Technology Tamil Nadu Chennai600119 India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350359299

The integration of Electroencephalography (EEG) and Brain-computer Interface (BCI) technologies is causing an evolution in healthcare, accessibility, and neuroscience. This multidisciplinary method offers a non-invasive means to communicate and control through intentional eye movements, which is particularly promising for patients suffering from neurological illnesses. In this study, eye movements are identified using EEG data from the EPOC Flex wireless EEG brain device. Using advanced Deep Learning models such as the Deep Belief Network (DBN) and the Deep Residual Network (Deep ResNet), and attempted to distinguish four distinct eye movements: open, close, right, and left. Some of the primary metrics utilized for assessing these models were accuracy, precision, recall, and F1 score. The Deep ResNet model gives better results with an accuracy of 96.25%, recall of 95.85%, precision of 96.66%, and an F1 score of 96.24%. The findings suggest that BCI frameworks can leverage EEG-based eye movement detection to improve rehabilitation procedures and make computers and devices easier to operate. This study's findings open a path for future research into neurotechnological applications and human-computer interaction. © 2024 IEEE.

关键词： Eye movements

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design of half adder using integrated leakage power reduction techniques

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Materials Today: Proceedings 2022年 69卷 576-581页

作者： Bindu Katikala, Hima Pavan Kumar, Thatha Manideep Reddy, Bhimavarapu V.V.Pavan Kumar, Bandireddy Ramana Murthy, G. Dixit, Saurav Department of Electronics and Communication Engineering Vignan's Foundation for Science Technology and Research 522213 India Department of E.C.E Department of Electronics and Communication Engineering Alliance College of Engineering and Design Alliance University Bangalore 562106 India Peter the Great St. Petersburg Polytechnic University St Petersburg 195251 Russia Division of Research & Innovation Uttaranchal University Dehradun 248007 India

The necessity for the development of compact, portable, and reliable electronic devices of enhanced speed and efficiency has prompted the scaling of CMOS devices to be indispensable. However, the benefit of scaling CMOS devices comes at the cost of increased leakage current in circuits. The variance in power consumption by these circuits incites detrimental impacts on the operational characteristics of the entire device. So, in this work, a novel leakage power reduction technique obtained by combining the Leakage Control Transistor (LECTOR) approach and drain gating approach is proposed. Both these subthreshold leakage minimization approaches are prominently used in Complementary Metal Oxide Semiconductor (CMOS) devices for curtailing the leakage power. The effectiveness of the proposed Integrated Drain Gating Lector (IDGL) technique in mitigating the leakage power is ascertained by designing a half adder circuit. Hence the overall leakage power is of 3.16nW & delay 69.12 µs in 180 nm technology, and in low scale technology of 90 nm the same leakage power decline to 2.19nW & delay is 65.45 µs. © 2022

关键词： Adders

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging

Supply Chain Management Using Internet of Things and Blockch...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： KanwalPreet Kour S Kanwal Deep Singh Malvinder Singh Bali G S Pradeep Ghantasala Rajesh Sharma R U Ananthanagu School of Computer Science and Engineering Manipal University Jaipur India Department of Computer Science Federation University Australia Department of Data Science and Engineering Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor.

关键词： Industries Supply chain management Supply chains Transportation Packaging Blockchains Safety Security Reliability Internet of Things

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AI-Driven Smart Health Monitoring System Using Wearable IoT Devices and Predictive Analytics 2

AI-Driven Smart Health Monitoring System Using Wearable IoT ...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Prashanthi, Bh. Sharma, Ashish Mahima, M. Navya, K. Al-Fatlawy, Ramy Riad Chidambaranathan, Senthilnathan GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India CMR Institute of Technology Department of ECE Telangana Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Virtusa Corporation Department of Architecture and Design NJ United States

ISBN: (纸本)9798350368109

The demonstrative realisation of this research is the smart health monitoring system, proposed and designed to employ wearable IoT technology and predictive analysis for constant health check. The system is intended to gather the physiological data;for example, pulse, blood pressure, and oxygen saturation and others via wearables and process it using sophisticated AI techniques and approaches, including deep learning based on CNN and LSTM. According to the systems, there are predictions that need to be offered by means of the programs and health risks that should be anticipated for timely intervention in order to increase chances for the patients' recovery. Although the system is designed for home use, the writers applied a cloud-based solution that provides scalability and real-time data processing;data is securely transferred from the wearable devices to the cloud through Bluetooth and Wi-Fi. This physiological data is augmented by the other public datasets available in the healthcare domain such that the AI learning algorithms can learn from an extensive health record. From the above research, it manifests how the system was able to predict serious health precursors including;heart attack and respiratory failure with LSTM models having prediction rate of more than 96%. It also delivers on-time alarms to the clinical practitioners, with average response time of less than three seconds for critical states. A set of surveys concerning the usability and comfort of the wearable devices among the patients has been conducted and the results show high satisfaction levels reported by the patients about the accuracy as well as simplicity of the system. Despite the introduced effort of the system, there exist significant concerns involving data protection, compatibility, and AI ethic;therefore, the future study should overcome such shortcomings. With these advancements of an artificial intelligence driven health monitoring system, healthcare systems would shift to being mor

关键词： Blood pressure

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Advancements in Brain Disease Diagnosis for Comparing Traditional Methods and Deep Learning Approaches in Medical Imaging

Advancements in Brain Disease Diagnosis for Comparing Tradit...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Sunita Kumari Laxman Singh Moghal Irfan Pasha M. Kumaraswamy Amar Choudhary Monika Agarwal Department Computer Science and Engineering ABES Institute of Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE-AI&ML CVR College of Engineering Hyderabad India Department of CST Sasi Institute of Technology and Engineering Tadepalligudem India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

This work explores improvements in the diagnosis of brain disease through traditional vs deep learning base techniques with respect to Medical Imaging. The dataset containing 3,000 images of brain tumors was preprocessed to resize (224×224), normalize and augmentation techniques like rotation, flipping were further implemented. The dataset are splitted into70% training and 30%. testing across four deep learning models, Convolutional Neural Networks (CNN), VGG16. The models were trained to classify brain tumor images into more or less complex categories. After training VGG19 gave top accuracy of 98.6 per cent, followed byVVG16 at second place with an accuracy rate of 96.75%, while the ResNet50 and CNN were rated third and fourth in terms of their accuracies; as neither performed well with just 92.3% and 89.3%. VGG19 showed the best precision (98.8%), recall(98.5%) and F1-score(98.65) among metrics of all models utilised in this research(which ever they might be). VGG19 had low false positives and negatives which are important for accurate diagnosis as can be seen in the confusion matrices. The type of research exemplifies how deep learning can improve the accuracy and certainty within medical imaging diagnostics, particularly in difficult tasks such as brain tumor classification. This study highlights the strong possibilities deep learning could lead to transformative improvements in clinical neuroscience by employing advanced neural network architectures and meticulous preprocessing strategies.

关键词： Deep learning Training Accuracy Brain tumors Brain modeling Feature extraction Convolutional neural networks Medical diagnosis Biomedical imaging Residual neural networks

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A Broadcast Based Link Discovery Scheme for Minimizing Messages in Software Defined Networks

A Broadcast Based Link Discovery Scheme for Minimizing Messa...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Hussain, Mir Wajahat Moulik, Soumen Roy, DIptendu Sinha Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Anekal562106 India National Institute of Technology Meghalaya Department of Computer Science and Engineering Laitumkhrah Shillong793003 India

ISBN: (纸本)9781665423908

Software defined network (SDN) with disjoint control and data plane enables the controller to address network requirement better. A significant issue in such networks is how the network resources are allocated by the controller based on the demand, for instance, allocation of path with less delay, less error rate and so forth. The controller allocate such network resources by enabling link discovery between switches. SDN utilizes protocol like link layer discovery protocol (LLDP) for links to be identified in OpenFlow devices. The detection of links in OpenFlow devices requires a huge number of PACKET OUT (Pout) messages from the controller to be sent across every active interfaces of the switch which might limit the controller performance. This paper introduces a Broadcast based Link Discovery (BBLD) scheme which lowers messages required to obtain the link discovery in OpenFlow switches (OFS's) by dispatching a solitary Pout from the SDN controller (unlike a unicast message from the controller in case of LLDP) to any OpenFlow switch (OFS), which then circulates the same to the nodes successively. Experiments performed with Mininet reveal that messages required to obtain similar link discovery are reduced by an average of (28%-42.4%) under varied set of topologies. © 2021 IEEE.

关键词： Controllers

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Early Diabetes Prediction Through Multiple Machine Learning Models: A Comparative Analysis

Early Diabetes Prediction Through Multiple Machine Learning ...

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AI-Driven Smart Healthcare for Society 5.0

作者： Anuja Sharma G S Pradeep Ghantasala Laishram Bijialuxmi Devi Pradeepta Kumar Sarangi Rajesh Sharma R Akey Sungheetha College of Nursing AIIMS Bilaspur Himachal Pradesh Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Chitkara University College of Nursing Chitkara University Himachal Pradesh India Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

People of every stage of life are affected by diabetes, which has emerged as a significant health issue in modern society. A total of 463 million individuals between the ages of 20 and 79 were estimated to have diabetes in 2019, and projections suggest that figure might rise to 700 million by 2045. Healthcare systems face serious difficulties as a result of this spike, which raises the need for medical treatment, increases disability rates, and causes more early deaths. Diabetes is linked to a number of health problems, including cardiovascular disorders, renal problems, and eyesight loss. Early identification and treatment, however, can lessen or delay these issues. Four ML models are used in this work to categorize and forecast diabetes: DT, XGBoost, LR, and RF. The results indicate that the XGBoost model outperforms the others, achieving an accuracy rate of 96.04%.

关键词： Radio frequency Training Analytical models Accuracy Scalability Medical treatment Machine learning Predictive models Diabetes Faces

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Prediction of Cardiac Disease Using Naive Bayes Algorithm

Prediction of Cardiac Disease Using Naive Bayes Algorithm

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S.M. Ramesh Sengottaiyan. N Vanathi. D Manoja. R Tamizharasu. K Kalyanasundaram. P Department of ECE KPR Institute of Engineering and Technology Coimbatore Faculty of Science and Technology Alliance College of Engineering and Design Alliance University Anekal Karnataka Department of CSE Nandha Engineering college Perundurai Erode Department of CSE (CS) K S R College of Engineering Tiruchengode Tamilnadu Department of Computer Science K S R College of Engineering Tiruchengode Tamilnadu Department of IT K S R College of Engineering Tiruchengode Tamilnadu

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The goal of this work is to use machine learning methods, such as the Naive Bayes algorithm, to accurately forecast cardiac disease. The proposed work implements K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Naïve Bayes to perform regression and classification analysis on the data to attain improved accuracy. This revised dataset is used to train the models, and standard metrics are used to thoroughly assess the model's predicted performance. With an accuracy of 88%, the naive bayes algorithm performed better than the KNN algorithm. In the context of forecasting cardiac disorders, this conversation offers a thorough review of the Naive Bayes method. The implementation generates accurate predictions based on crucial health factors through data preprocessing, feature selection and model training.

关键词： Support vector machines Training Accuracy Machine learning algorithms Cardiac disease Machine learning Predictive models

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JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep Learning

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SN computer Science 2023年第6期4卷 840页

作者： Verma, Aryan Gupta, Nidhi Bhatele, Pushpraj Khanna, Pritee Department of Computer Science and Engineering National Institute of Technology HP Hamirpur 177005 India Department of Computer Applications National Institute of Technology HR Kurukshetra 136119 India NSCB Medical College MP and MRI Scan Centre Nagpur Road MP Jabalpur 482003 India Computer Science and Engineering Discipline PDPM Indian Institute of Information Technology Design and Manufacturing MP Jabalpur 482005 India

The growth of abnormal cells in the brain gives rise to a deadly form of cancer known as a brain tumor. The mass of brain tumors proliferates and rises very fast, and if not appropriately treated, the patient’s survival rate is less or can rapidly lead to death. A very exigent task for radiologists is early brain tumor detection which may help to evaluate the tumor and plan treatment for an effective prognosis. In this work, we used deep learning to detect and localize brain tumors from MRI slices. After the detection of the tumor, we generate a color heatmap to show the tumor’s location, severity, and possible affected regions. Our novel image processing algorithm swiftly segments the skull region from the background of MR images, augmented over heatmap for better observation. We analyzed the performance of VGG 16, Resnet 50, and EfficientNet deep learning architectures. Transfer learning on Resnet 50 model with custom top layers achieved excellent performance with 99.37% accuracy, 99.68% precision, and 99.69% specificity, which is better than most of the works in brain tumor detection using machine learning. Expert radiologists critically examined our model outcomes in detail. They found our method effective to triage people with brain tumors and helpful in decision making for the clinicians by severity indication through heatmaps. They observed the prime cause for false positives to be unbalanced contrast in some MR images. Through this work, we also made the novel dataset named as JMCD (Jabalpur Medical college Dataset) publicly available for research which contains all four MR pulse sequences annotated by expert radiologists for 140 patients. Each patient has 15–20 sequences containing 250 images. Through this work and accompanying data, we aim to give better methods for brain tumor detection and help research in the same field. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Brain tumor Deep learning Glioma Medical image analysis MRI

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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