Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach

With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance

Tumor Segmentation and Classification Using Machine Learning Approaches

Medical image processing has recently developed progressively in terms of methodologies and applications to increase serviceability in health care management. Modern medical image processing employs various methods to diagnose tumors due to the burgeoning demand in the related industry. This study uses the PG-DBCWMF, the HV area method, and CTSIFT extraction to identify brain tumors that have been combined with pancreatic tumors. In terms of efficiency, precision, creativity, and other factors, these strategies offer improved performance in therapeutic settings. The three techniques, PG-DBCWMF, HV region algorithm, and CTSIFT extraction, are combined in the suggested method. The PG-DBCWMF (Patch Group Decision Couple Window Median Filter) works well in the preprocessing stage and eliminates noise. The HV region technique precisely calculates the vertical and horizontal angles of the known images. CTSIFT is a feature extraction method that recognizes the area of tumor images that is impacted. The brain tumor and pancreatic tumor databases, which produce the best PNSR, MSE, and other results, were used for the experimental evaluation

Deep learning algorithms and their relevance: A review

Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. This paper discusses deep learning and various supervised, unsupervised, and reinforcement learning models. An overview of Artificial neural network(ANN), Convolutional neural network(CNN), Recurrent neural network (RNN), Long short-term memory(LSTM), Self-organizing maps(SOM), Restricted Boltzmann machine(RBM), Deep Belief Network (DBN), Generative adversarial network(GAN), autoencoders, long short-term memory(LSTM), Gated Recurrent Unit(GRU) and Bidirectional-LSTM is provided. Various deep-learning application areas are also discussed. The most trending Chat GPT, which can understand natural language and respond to needs in various ways, uses supervised and reinforcement learning techniques. Additionally, the limitations of deep learning are discussed. This paper provides a snapshot of deep learning

Consideration of Data Security and Privacy Using Machine Learning Techniques

As artificial intelligence becomes more and more prevalent, machine learning algorithms are being used in a wider range of domains. Big data and processing power, which are typically gathered via crowdsourcing and acquired online, are essential for the effectiveness of machine learning. Sensitive and private data, such as ID numbers, personal mobile phone numbers, and medical records, are frequently included in the data acquired for machine learning training. A significant issue is how to effectively and cheaply protect sensitive private data. With this type of issue in mind, this article first discusses the privacy dilemma in machine learning and how it might be exploited before summarizing the features and techniques for protecting privacy in machine learning algorithms. Next, the combination of a network of convolutional neural networks and a different secure privacy approach is suggested to improve the accuracy of classification of the various algorithms that employ noise to safeguard privacy. This approach can acquire each layer's privacy budget of a neural network and completely incorporates the properties of Gaussian distribution and difference. Lastly, the Gaussian noise scale is set, and the sensitive information in the data is preserved by using the gradient value of a stochastic gradient descent technique. The experimental results showed that a balance of better accuracy of 99.05% between the accessibility and privacy protection of the training data set could be achieved by modifying the depth differential privacy model's parameters depending on variations in private information in the data

An Overview of Manet Power Management Approaches

One of the primary issues with MANET is power optimization and utilization because it relies on the node's internal battery power to operate the wireless network. The performance of the MANET is also affected by one of the parameters of energy consumption and utilization. Each operation in the MANET requires some amount of energy to complete. This article elaborated on MANET power management from its inception to the present, as well as doing comparison research to recommend new methods for improving MANET power utilization. This study examines MANET power management options in terms of numerous parameter metrics, including Mobility Aware, Clustering, Topology, Transmission Range, QOS, and link-based. Finally, the methodologies used in MANET power management and performance factor improvement were summarised. To surpass all performance indicators in MANET utilization, new manipulative tactics are necessary. The innovative method is the most effective

Machine Learning Techniques for Lung Cancer Risk Prediction using Text Dataset

The early symptoms of lung cancer, a serious threat to human health, are comparable to those of the common cold and bronchitis. Clinical professionals can use machine learning techniques to customize screening and prevention strategies to the unique needs of each patient, potentially saving lives and enhancing patient care. Researchers must identify linked clinical and demographic variables from patient records and further pre-process and prepare the dataset for training a machine-learning model in order to properly predict the development of lung cancer. The goal of the study is to develop a precise and understandable machine learning (ML) model for early lung cancer prediction utilizing demographic and clinical variables, as well as to contribute to the growing field of medical research ML application that may improve healthcare outcomes. In order to create the most effective and precise predictive model, machine learning techniques like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbor (KNN), and Naive Bayes were utilized in this article

Towards Designing a Planet Walk Simulation in a Controlled Environment

Planet Walk Simulation involves several parametric readings like physical human body conditions, geographic and atmospheric conditions, etc. In this work, we have set goals including system state monitoring, terrain mapping, and user navigation. Specific objectives are displaying the user’s telemetry, elevating their spatial awareness through short range object detection, and displaying their location relative to origin using wi-fi routers. The experimental results showed navigation motion paths for astronauts and identify obstacles in the path with the help of LiDAR and Hololens

Intepretable Deep Gaussian Naive Bayes Algorithm (Idgnba) Based Task Offloading Framework for Edge-Cloud Computing

When it comes to Internet of Things (IoT) applications and machine learning based computing, resource-restricted edge devices are inadequate due to the exponential growth of mobile information and the massive need for processing power. An edge offload, the migration of complex tasks from IoT devices to edge cloud servers, is a distributed computing paradigm that has the potential to overcome the IoT device resource limits, lessen the computational load, and increase the effectiveness with which activities are processed. However, due to the NP-hard nature of the optimum offloading decision-making issue, an efficient solution using traditional optimization techniques is difficult. Current deep learning algorithms still have a lot of problems, such as their slow pace of learning and limited ability to adapt to new environments. We provide a unique interpretable deep Gaussian naive Bayes technique (IDGNBA) for extremely fine offloading choices to address these issues. Through several simulation studies, we assess the efficacy of IDGNBA and find that it performs better in terms of offloading than traditional techniques. The model has strong mobility and can quickly adjust to a fresh MEC working atmosphere while taking offloading decisions in real-time

Using Federated Artificial Intelligence System of Intrusion Detection for IoT Healthcare System Based on Blockchain

Recently Internet of things (IoT)-based healthcare system has expanded significantly, however, they are restricted by the absence of an intrusion detection mechanism (IDS). Modern technologies like blockchain (BC), edge computing (EC), and machine learning (ML) provide a robust security solution that is well-suited to protecting patients' medical information. In this study, we offer an intelligent intrusion detection mechanism FIDANN that protects the confidentiality of medical data by completing the intrusion detection task by utilising Dwarf mongoose-optimized artificial neural networks (DMO-ANN) through a federated learning (FL) technique. In the context of recent developments in blockchain technology, such as the elimination of contaminating attacks and the provision of complete visibility and data integrity over the decentralized system with minimal additional effort. Using the model at the edges secures the cloud from attacks by limiting information from its gateway with less computing time and processing power as FL works with fewer datasets. The findings demonstrate that our suggested models perform better when dealing with the diversity of data produced by IoT devices

A Big Data Analytical Framework for Intrusion Detection Based On Novel Elephant Herding Optimized Finite Dirichlet Mixture Models

For the purpose of identifying a wide variety of hostile activity in cyberspace, an Intrusion Detection System (IDS) is a crucial instrument. However, traditional IDSs have limitations in detecting zero-day attacks, which can lead to high false alarm rates. To address this issue, it is crucial to integrate the monitoring and analysis of network data with decision-making methods that can identify anomalous events accurately. By combining these approaches, organizations can develop more effective cybersecurity measures and better protect their networks from cyber threats. In this study, we proposed a novel called the Elephant Herding Optimized Finite Dirichlet Mixture Model (EHO-FDMM). This framework consists of three modules: capture and logging, pre-processing, and an innovative IDS method based on the EHO-FDMM. The NSL-KDD and UNSW-NB15 datasets are used to assess this framework's performance. The empirical findings show that selecting the optimum model that accurately fits the network data is aided by statistical analysis of the data. The EHO-FDMM-based intrusion detection method also offers a lower False Alarm Rate (FPR) and greater Detection Rate (DR) than the other three strong methods. The EHO-FDMM and exact interval of confidence bounds were used to create the suggested method's ability to detect even minute variations between legal and attack routes. These methods are based on correlations and proximity measurements, which are ineffective against contemporary assaults that imitate everyday actions