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Leveraging Machine Learning to Identify Synergistic Drug Combinations for Effective Cancer Treatment

Leveraging Machine Learning to Identify Synergistic Drug Com...

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International Conference on Image Information Processing

作者： P. Sujatha K Saravanan Mohammed Ali Sohail A Basi Reddy Rohit R Dixit Nallam Krishnaiah Department of AI/ML Amrita Vishwa Vidyapeetham Amaravati Guntur India Department of Information Technology R.M.K Engineering College Tiruvallur District Tamil Nadu India Department of Computer & Network Engineering College of Computer Science & Information Technology Jazan University Jazan K.S.A Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Senior Data Scientist Siemens Healthineers New Hampshire USA Department of AI&ML St.Martin’s Engineering College Dhulapally Secunderabad Telangana India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The potential of drug combinations to treat and overcome medication resistance complex genetic diseases is evident. Synergistic drug combinations offer a promising way to enhance drug therapy efficacy and reduce the required medication dosage. However, developing effective combination medication therapies with synergistic effects has been challenging, despite numerous ongoing clinical investigations. Current models and approaches to detect medication synergy outlined in the literature lack the expected consistency in outcomes. to better comprehend the impact of particular medication combinations, it is essential to be familiar with the vocabulary used to describe synergy. In this study, a combinational drug screen is utilized to identify useful features for locating synergistic or efficient drug combinations. The feature selection algorithm (Boruta) helps select the most relevant features, and machine learning models are then trained using the selected feature dataset. Performance assessment metrics like sensitivity, accuracy, and specificity are used to compare the trained models, and the Random Forest model stands out for its significantly better performance compared to other models.

关键词： Drugs Vocabulary Sensitivity Machine learning algorithms Cancer treatment Predictive models Feature extraction

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Design of Dual-Stack, Tunneling, and Translation Approaches for Blockchain-IPv6

Design of Dual-Stack, Tunneling, and Translation Approaches ...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2021

作者： Mantena, Srihari Varma Jayasundar, S. Sharma, Dilip Kumar Veerappan, J. Bennet, M. Anto Sengan, Sudhakar Rangasam, Rajasekar Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Computer Science and Engineering Idhaya Engineering College for Women Tamil Nadu Chinnasalem India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna India Department of Electronics and Communication Engineering Saveetha School of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Computer Science and Engineering-AI & ML School Engineering Malla Reddy University Hyderabad India

ISBN: (纸本)9789811900105

The most current edition of the Internet protocol is IPv6 (IP). Because once compared to IPv4 and IPv6 has a series of benefits of more efficient packet processing. With each day, IPv4 addresses should become obsolete. Migration from IPv4 to IPv6 is now required. This study uses tunneling, dual-stack, and translation methods to transition from IPv4 to IPv6. Using such techniques, we could send a limited time from an IPv4 user to an IPv6 client. IPv4 packets on an IPv4 network and IPv6 packets on an IPv6 network are transmitted using a dual-stacked method. The proposed tunneling method uses the same channel for data transfer in IPv6 and IPv4 packets. For IPv4 to IPv6 transmission, a translation method has also been implemented. The dual-stack, tunneling, and translation method has high inter-communication versatility, efficiency, and consistency. Only with a wire shark’s help, the procedure of this channel be examined (a packet analyzer). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Wide-Deep Neural Networks with Randomized Sparse Backpropagation Simplify Demand-Side Load Event Detection

Wide-Deep Neural Networks with Randomized Sparse Backpropaga...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Adusumilli, Manasa Ramesh, G.P. Katkam, Vijayrani Bhargavi Pareek, Piyush Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India St. Peter's Institute for Higher Education and Research Department of Electronics and Communication Engineering Chennai India Ballari Institute of Technology and Management Department of e and Ce Bellary India Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

Identifying demand-side load events is a critical step in lowering energy use in a number of situations. Using wide-deep neural networks and randomised sparse backpropagation algorithms, this study presents a unique way to identify demand-side load events. To detect subtleties in the input data, we employ randomised sparse backpropagation and both shallow and deep network topologies. We also use the ReLU and sigmoid activation functions to improve the performance of our model. We use the Adam optimizer to train the proposed model, using a learning rate of 0.001 per batch and a batch size of 64. The results were compared to those of other popular technologies, such as support vector machines. According to the findings of our studies, the technique we utilised is more precise and efficient than any other option we investigated. The proposed approach combines wide-deep neural networks with randomised sparse backpropagation to simplify demand-side load event identification. © 2023 IEEE.

关键词： Energy efficiency

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Ensemble Deep Learning Framework for Classification of Skin Lesions

Ensemble Deep Learning Framework for Classification of Skin ...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Shantakumar B Patil H V Ramana Rao Chatrapathy K Ajmeera Kiran A Shiva Kumar Pundru Chandra Shaker Reddy Computer Science and Engineering Sai Vidya Institute of Technology Rajanukunte Bengaluru Karnataka India Computer Science and Engineering (AI&ML) Geethanjali College of Engineering and Technology Hyderabad India School of Computing &Information Technology REVA University Bengaluru Karnataka India Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India Computer Science and Engineering CMR College of Engineering & Technology Hyderabad Telangana School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

Skin cancer is one of the majority prevalent malignancies that can be diagnosed visually and then further investigated with dermoscopic analysis and other procedures. Several skin-lesion (SL) classification methods using deep learning (DL) based on convolution-neural-network (CNN) and annotated skin photos illustrate modified results because visual observation provides the opportunity to use artificial-intelligence (ai) to intercept the various skin images at an early stage. In this regard, the research proposes a trustworthy method for identifying skin cancer using dermoscopy images, with the goal of bettering the visual perception and diagnostic abilities of medical experts. SL region of interest (RoI) segmentation from dermoscopy pictures was performed using swarm intelligence (SI) algorithms, and the RoI marked as the best segmentation oucome was subjected to feature extraction using the Grasshopper Optimization Algorithm (GOA). Using CNN and the ISIC-2017, ISIC-2018, and PH-2 data sets, the SLs are divided into two categories. Classification accuracy-sensitivity,-specificity-F -measure, precision, MCC, dice coefficient, and Jaccard index are used to evaluate the results of the proposed segmentation and classification strategies, with an average of 98.42%, 977.33%, and 0.97040%, respectively. Our proposed model outperforms the state-of the-art in every metric we considered

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Intelligent Resource Allocation for Edge-IoT: Enhancing QoE with Deep Reinforcement Learning

Intelligent Resource Allocation for Edge-IoT: Enhancing QoE ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： B Sreedhar C Sasikala V T Ram Pavan Kumar M Ruhisulthana Shaik Syed Muqthadar Ali Yadaiah Balagoni Department of CSE (AI&ML) G Pulla Reddy Engineering College Kurnool India Department of CSE Srinivasa Ramanujan Institute of Technology Ananthapuramu India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India Department of ECE Sir C Ramalinga Reddy Polytechnic College Eluru India Department of CSE CVR College Of Engineering Hyderabad India Department of Emerging Technologies Mahatma Gandhi Institute of Technology Gandipet India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

A resource allocation framework zeroed in on Quality of Experience (QoE) that is explicitly intended for the heterogeneous edge Internet of things (IoT) environment's dynamic and fluctuated situation. Applications for the Internet of Things (IoT) are confronting edge figures. Be that as it may, because of the heterogeneity of usages, the edge cloud finds it trying to relegate complex compelled resources (handling, memory, limit, information transmission, etc) with limitations in light of the Quality of Service (QoS) necessities of clients. In this exploration, we tackle the issue of resource allocation in Edge-IoT structures by supporting a canny framework called Deep Edge that assigns resources to the different IoT applications with the sole motivation behind working on the Quality of Experience (QoE) of the clients. To accomplish this objective, we create a complex QoE model that considers adjusting the different prerequisites of Internet of Things applications to the accessible edge resources. The arrangement is accomplished by choosing a QoS necessity arrive at that the accessible resources can meet. Moreover, we propose a brilliant two-stage deep reinforcement learning (DRL) move toward that successfully allots edge resources to help IoT applications and further develop QoE for people. Not at all like the famous DRL, our arrangement utilizes deep neural networks (DNN) to design the Edge-IoT state to joint resource allocation movement, which incorporates resource allocation and QoS class. Along these lines, DNN can be used to deal with exercises' exploration. The cooperative exertion not just builds the quality of experience (QoE) of clients and fulfills the prerequisites of assorted applications, yet it likewise adjusts the QoS necessities to the accessible resources.

关键词： Image edge detection Quality of service Artificial neural networks Information processing Deep reinforcement learning Dynamic scheduling Quality of experience Internet of Things Resource management Edge computing

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Applying Convolutional Neural Networks for Disease Detection in Crop Images

Applying Convolutional Neural Networks for Disease Detection...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gulshan Dhasmana Hassan M. Al-Jawahry A. Deepak K Aravind R. Akhilesh Reddy Kanchan Yadav Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana Department of Electrical Engineering GLA University Mathura

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Ensuring agricultural production and food security relies on the early and precise identification of crop diseases. It may be tedious, error-prone, and time-consuming to use traditional approaches to identify diseases. In this study, we investigate how Convolutional Neural Networks (CNNs) may be used to improve and automate the process of identifying diseases in crop photos. Our approach use deep learning to swiftly and reliably identify and classify a broad variety of agricultural diseases. We trained our CNN model on a diverse dataset that included images of crop leaves in various states of health. The dataset covers a broad variety of common illnesses and numerous kinds of crops, which guarantess that our model is resilient and can be applied to other situations. To avoid overfitting and boost the model’s performance, we used sophisticated data augmentation methods. The CNN model surpasses traditional machine learning methods in terms of accuracy, as shown by a significant reduction in false positives and false negatives. The model’s capacity to process and analyse images in real-time makes it beneficial in the field of disease management. This research emphasises the possibility of incorporating convolutional neural networks (CNNs) into precision agricultural methods, giving farmers a formidable instrument for early disease detection and prompt response to reduce crop losses. Improving the model’s scalability for largescale agricultural applications, adding more disease kinds to the dataset, and increasing the dataset are all areas that will be the focus of future effort.

关键词： Training Quantization (signal) Accuracy Scalability Crops Production Real-time systems Convolutional neural networks Reliability Diseases

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Revolutionizing Cancer Diagnosis with Deep Learning: A Case Study Using U-Net

Revolutionizing Cancer Diagnosis with Deep Learning: A Case ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Himanshu Rai Goyal Mohammed Al-Farouni A. Deepak R. Akhilesh Reddy Kanchan Yadav V Revathi Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana Department of Electrical Engineering GLA University Mathura Department of Applied Sciences New Horizon College of Engineering Bangalore

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The U-Net architecture is a convolutional neural network model that is well-known for its skills in biomedical image segmentation. This study investigates the revolutionary potential that the U-Net architecture presents in the context of cancer diagnosis. We guarantee that the input is of high quality by using sophisticated preprocessing methods, which we do by constructing a robust $\mathbf{U}$-Net-based system that is specifically designed for the segmentation and classification of tumour regions in medical imaging data. An exhaustive examination that makes use of criteria such as the Dice similarity coefficient, precision, recall, and $F 1$ score indicates that our model performs substantially better than conventional techniques when it comes to identifying and distinguishing malignant tissues. Case studies that cover a wide range of cancer types provide further validation of the flexibility and practical application of the system. These studies also highlight the significant influence that deep learning has on increasing diagnosis accuracy, lowering the risk of human error, and overall patient outcomes in the field of cancer treatment.

关键词： Deep learning Image segmentation Accuracy Liver computer architecture Brain modeling Convolutional neural networks Cancer Tumors Biomedical imaging

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A Predictive Analytics Approach to Traceability in Digital Forensics Using Time Series Analysis

A Predictive Analytics Approach to Traceability in Digital F...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： K Srujan Raju R. Ahila Mohammed Al-Farouni Gotlur Karuna Kanchan Yadav Neelam Sanjeev Kumar Department of CSE CMR Technical Campus Hyderabad Telangana India Department of CSE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML GRIET Bachupally Hyderabad Telangana India Department of Electrical Engineering GLA University Mathura Department of Computer Science & Engineering (E.Tech.) SRM Institute of Science & Technology Vadapalani Chennai

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Digital forensics is essential to cybersecurity and the examination of virtual activities –particularly criminal ones. This research outlines a predictive analytical technique in his based on the time series to improve digital forensics traceability. The performance of the research is assessed by using the Digital Forensic Investigation Dataset (DFID) which contains application logs, network traffic files and file system images derived from inv You can download simulated forensic cases which are time stamped application logs, network traffic records and file system snapshots which are used to evaluate the performance of the research through Digital Forensic Investigation Dataset (DFID). In this regard, machine learning and deep learning algorithms such as Random Forest, Support Vector Machines, Long Short-Term Memory Networks were used to anticipate and detect the notion of suspicious action. Concerning the results of the Random Forest model, it yielded a high accuracy of 95% and precision of 94%, along with a recall of 96%, F1-score of 95%, and AUC of 0. 98. The feature importance analysis emphasized the most important features in the forensic process such as time, Internet protocol addresses, file hashes, and types of events. This research contributes to enhancing the knowledge base in the field of digital forensics by proposing the use of predictive analytics for early identification and mitigation of threats; this forms part of making Investigations efficient. The results support the notion that informational knowledge, as well as currently practiced and emerging computational methods, should play a central role in forensic practices. Some of the areas of further work are the attempt to combine the approaches and the refinement of the deep learning schemes to faces more complicated circumstances that can be encountered in forensics, as well as the integration of legal and ethical issues to the use of predictive models within digital environment of forensic inves

关键词： Deep learning Support vector machines Accuracy Digital forensics Time series analysis Telecommunication traffic Predictive models Mathematical models Predictive analytics Random forests

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Enhancing Alzheimer’s Disease Diagnosis Through Deep Learning-Leveraging VGG-16 Architecture and Adaptive Boosting

Enhancing Alzheimer’s Disease Diagnosis Through Deep Learni...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Harshavelu Irrigisetty K. Reddy Madhavi J. Avanija Rishi Reddy Thokala K S S Rishi Varma Department of CSE Sree Vidyanikethan Engineering College Tirupati India Department of AI&ML Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati India School of Computing Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of CSE SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

The key to early intervention and patient care with Alzheimer’s disease is early detection. The World Health Organization estimates that 50 million people worldwide suffer from dementia, with $60-70 \%$ of cases being Alzheimer’s disease (AD). Moreover, the WHO projects that the number of people affected by AD will be nearly tripled by the year 2050, thus, the development of effective detection and management strategies. Our Research suggests a complete method that combines Convolutional Neural Networks (CNNs) with transfer learning using the VGG16 architecture, on the one hand, and Adaptive Boosting on the other, in order to improve the accuracy. The dataset includes NonDemented, VeryMildDemented, MildDemented, and ModerateDemented classes which makes the VGG16 model suitable for extraction of features and classification after the preprocessing steps, such as image resizing, conversion to arrays and pixel value normalization, are performed. From the problems faced in the earlier AD detection methodologies we use CNNs, transfer learning and adaptive boosting to improve the task performance. In conclusion, the combination of automated computational methods and sophisticated algorithms such as CNNs and AdaBoost is a progressive approach in AD detection. It provides services like efficiency, consistency, and improved diagnosis that can be applied to the patient care and the healthcare management.

关键词： Adaptation models Adaptive systems Transfer learning computer architecture Organizations Boosting Feature extraction

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Improved Sail Fish Optimizer for Energy Efficient Routing in Wireless Sensor Network

Improved Sail Fish Optimizer for Energy Efficient Routing in...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Aluvala, Srinivas Reddy, Geetha Al-Jawahry, Hassan M. Ramadan, Ghazi Mohamad Pareek, Piyush Kumar SR University School of Computer Science & Artificial Intelligence Warangal India CSE Malla Reddy Engineering College for Women Hyderabad India The Islamic University Najaf Iraq Ahl Al Bayt University College of Mlt Karbala Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350315455

A Wireless Sensor Network (WSN) is a collection of nodes fitted with small sensors and transceiver elements that are utilized for sensing, tracking, and data collection in a variety of situations. Sensor energy is considered a significant resource due to the difficulty in providing battery a backup to sensors when they are deployed in an uncontrolled or wild area. Integrating clustering & routing strategies are the optimal techniques for saving sensor node energy. In this research, the Improved Sail Fish Optimizer (ISFO) is proposed for the energy efficient routing path selection in WSN to improve the network lifetime. The ISFO algorithm is utilized to identify an accurate routing to sink node for data transmission. The performance of proposed algorithm is evaluated with performance metrics of Packet Delivery Ratio (PDR), Throughput, energy consumption and delay. The proposed algorithm attained high PDR of 96%, high throughput of 97%, less energy consumption of 0.4J and less delay of 3ms which is superior than other existing algorithms. © 2023 IEEE.

关键词： Clustering Improved Sail Fish Optimizer Routing protocol Sensor Nodes and Wireless Sensor Network

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