ganesh davanam (40 risultati)

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Taschenbuch. Condizione: Neu. NOVEL DEEP LEARNING ALGORITHMS FOR ANALYSING OF CRYPTOCURRENCY | APPLICATIONS OF NOVEL DEEP LEARNING ALGORITHMS FOR ANALYSING OF CRYPTOCURRENCY | Ganesh Davanam (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208452926 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Effective Solutions for Cross Layer Attacks in Cognitive Radio Network | Detection of Malicious Users during Cross Layer Attacks | Ganesh Davanam (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204748757 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condizione: Neu. Effektive Lösungen für schichtenübergreifende Angriffe in kognitiven Funknetzen | Erkennung von böswilligen Benutzern bei schichtenübergreifenden Angriffen | Ganesh Davanam (u. a.) | Taschenbuch | 116 S. | Deutsch | 2022 | Verlag Unser Wissen | EAN 9786204810904 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condizione: new. Paperback. With the worldwide spread of cryptocurrencies, data has become richer and more heterogeneous than before and, as a result, poses a new problem related to prediction and decision making. Although there has been recent increase in acceptance of deep learning (DL) technologies, the current analytical models appear to have limited correspondence with cryptocurrency markets. This work directly addresses price forecasting, fraud detection, sentiment analysis, and risk management, thus illustrating the applicability of the current technologies. We implement complex and novel deep learning techniques such as long short-term memory, gated recurrent unit, Bidirectional-LSTM to explore the temporal and spatial behaviour of data associated with cryptocurrencies. These models enable accurate predictions of price and market trends along with reinforcement strategies to refine trading strategies and generative models to assess the market and project what the future might require. This is used to help investors and traders spot patterns in the buying and selling of various crypto currencies, these models might have far-reaching effects on the economy. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 80 pp. Englisch.

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Cognitive Radio Networks (CRNs) have come out as an encouraging next-generation network technology that addresses the issues related to dynamic Spectrum Access and enhanced utilization of spectrum in a significant manner. Specifically Trust, Reputation Management models and Cross layer defense mechanism are more and more regarded for CRNs to secure them against the attacks posed by the secondary users. In this Work, a method called, Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) and Optimized Levensthein Cross layer Defense framework methods are proposed to secure the CRN by detecting the attackers at two different layers, Physical and Data link layers. Mean Bid Cross Layer Trust Evaluation model is applied to measure the trustworthiness of secondary user by third party. Followed by which, the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. Optimized Levesthein Nearest Centroid Framework (OS-LNCC) is proposed to mitigate Cross Layer attacks in CRN's. The performance of both the methods is evaluated by various parameters such as energy consumption, detection time, Sensing Delay, Throughput and detection accuracy. 96 pp. Englisch.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Paperback. Condizione: new. Paperback. With the worldwide spread of cryptocurrencies, data has become richer and more heterogeneous than before and, as a result, poses a new problem related to prediction and decision making. Although there has been recent increase in acceptance of deep learning (DL) technologies, the current analytical models appear to have limited correspondence with cryptocurrency markets. This work directly addresses price forecasting, fraud detection, sentiment analysis, and risk management, thus illustrating the applicability of the current technologies. We implement complex and novel deep learning techniques such as long short-term memory, gated recurrent unit, Bidirectional-LSTM to explore the temporal and spatial behaviour of data associated with cryptocurrencies. These models enable accurate predictions of price and market trends along with reinforcement strategies to refine trading strategies and generative models to assess the market and project what the future might require. This is used to help investors and traders spot patterns in the buying and selling of various crypto currencies, these models might have far-reaching effects on the economy. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. PRINT ON DEMAND.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Cognitive Radio Networks (CRNs) have come out as an encouraging next-generation network technology that addresses the issues related to dynamic Spectrum Access and enhanced utilization of spectrum in a significant manner. Specifically Trust, Reputation Mana.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -With the worldwide spread of cryptocurrencies, data has become richer and more heterogeneous than before and, as a result, poses a new problem related to prediction and decision making. Although there has been recent increase in acceptance of deep learning (DL) technologies, the current analytical models appear to have limited correspondence with cryptocurrency markets. This work directly addresses price forecasting, fraud detection, sentiment analysis, and risk management, thus illustrating the applicability of the current technologies. We implement complex and novel deep learning techniques such as long short-term memory, gated recurrent unit, Bidirectional-LSTM to explore the temporal and spatial behaviour of data associated with cryptocurrencies. These models enable accurate predictions of price and market trends along with reinforcement strategies to refine trading strategies and generative models to assess the market and project what the future might require. This is used to help investors and traders spot patterns in the buying and selling of various crypto currencies, these models might have far-reaching effects on the economy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the worldwide spread of cryptocurrencies, data has become richer and more heterogeneous than before and, as a result, poses a new problem related to prediction and decision making. Although there has been recent increase in acceptance of deep learning (DL) technologies, the current analytical models appear to have limited correspondence with cryptocurrency markets. This work directly addresses price forecasting, fraud detection, sentiment analysis, and risk management, thus illustrating the applicability of the current technologies. We implement complex and novel deep learning techniques such as long short-term memory, gated recurrent unit, Bidirectional-LSTM to explore the temporal and spatial behaviour of data associated with cryptocurrencies. These models enable accurate predictions of price and market trends along with reinforcement strategies to refine trading strategies and generative models to assess the market and project what the future might require. This is used to help investors and traders spot patterns in the buying and selling of various crypto currencies, these models might have far-reaching effects on the economy.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Las redes de radiocomunicación cognitiva (CRN) han surgido como una alentadora tecnología de red de próxima generación que aborda los problemas relacionados con el acceso dinámico al espectro y la utilización mejorada del mismo de manera significativa. En concreto, los modelos de gestión de la confianza y la reputación y el mecanismo de defensa de capa cruzada son cada vez más considerados para las CRNs con el fin de asegurarlas contra los ataques de los usuarios secundarios. En este trabajo, se propone un método llamado Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) y métodos de defensa de capa cruzada de Levensthein optimizados para asegurar la CRN detectando a los atacantes en dos capas diferentes, la física y la de enlace de datos. Se aplica el modelo de evaluación de confianza de capa cruzada Mean Bid para medir la fiabilidad del usuario secundario por parte de terceros. A continuación, se realiza la clasificación de usuarios maliciosos y normales aplicando el modelo de la Teoría del Juego de Nash Múltiple. Se propone un marco optimizado de centroide cercano (OS-LNCC) para mitigar los ataques de capa cruzada en las CRN. El rendimiento de ambos métodos se evalúa mediante varios parámetros, como el consumo de energía, el tiempo de detección, el retardo de detección, el rendimiento y la precisión de la detección. 116 pp. Spanisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Cognitive Radio Networks (CRNs) have come out as an encouraging next-generation network technology that addresses the issues related to dynamic Spectrum Access and enhanced utilization of spectrum in a significant manner. Specifically Trust, Reputation Management models and Cross layer defense mechanism are more and more regarded for CRNs to secure them against the attacks posed by the secondary users. In this Work, a method called, Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) and Optimized Levensthein Cross layer Defense framework methods are proposed to secure the CRN by detecting the attackers at two different layers, Physical and Data link layers. Mean Bid Cross Layer Trust Evaluation model is applied to measure the trustworthiness of secondary user by third party. Followed by which, the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. Optimized Levesthein Nearest Centroid Framework (OS-LNCC) is proposed to mitigate Cross Layer attacks in CRN's. The performance of both the methods is evaluated by various parameters such as energy consumption, detection time, Sensing Delay, Throughput and detection accuracy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cognitive Radio Networks (CRNs) have come out as an encouraging next-generation network technology that addresses the issues related to dynamic Spectrum Access and enhanced utilization of spectrum in a significant manner. Specifically Trust, Reputation Management models and Cross layer defense mechanism are more and more regarded for CRNs to secure them against the attacks posed by the secondary users. In this Work, a method called, Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) and Optimized Levensthein Cross layer Defense framework methods are proposed to secure the CRN by detecting the attackers at two different layers, Physical and Data link layers. Mean Bid Cross Layer Trust Evaluation model is applied to measure the trustworthiness of secondary user by third party. Followed by which, the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. Optimized Levesthein Nearest Centroid Framework (OS-LNCC) is proposed to mitigate Cross Layer attacks in CRN's. The performance of both the methods is evaluated by various parameters such as energy consumption, detection time, Sensing Delay, Throughput and detection accuracy.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Les réseaux radio cognitifs (CRN) sont apparus comme une technologie de réseau de nouvelle génération encourageante qui répond aux problèmes liés à l'accès dynamique au spectre et à l'utilisation améliorée du spectre de manière significative. Les modèles de gestion de la confiance et de la réputation ainsi que les mécanismes de défense intercouche sont de plus en plus considérés pour les CRN afin de les protéger contre les attaques des utilisateurs secondaires. Dans ce travail, une méthode appelée Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) et un cadre de défense intercouche optimisé de Levensthein sont proposés pour sécuriser le CRN en détectant les attaquants sur deux couches différentes, la couche physique et la couche de liaison de données. Le modèle Mean Bid Cross Layer Trust Evaluation est appliqué pour mesurer la fiabilité d'un utilisateur secondaire par un tiers. Ensuite, la classification des utilisateurs normaux et malveillants est effectuée en appliquant le modèle de la théorie des jeux de Nash multiples. Le cadre optimisé de Levesthein Nearest Centroid (OS-LNCC) est proposé pour atténuer les attaques entre couches dans les CRN. Les performances des deux méthodes sont évaluées par différents paramètres tels que la consommation d'énergie, le temps de détection, le délai de détection, le débit et la précision de détection. 116 pp. Französisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Las redes de radiocomunicación cognitiva (CRN) han surgido como una alentadora tecnología de red de próxima generación que aborda los problemas relacionados con el acceso dinámico al espectro y la utilización mejorada del mismo de manera significativa. En concreto, los modelos de gestión de la confianza y la reputación y el mecanismo de defensa de capa cruzada son cada vez más considerados para las CRNs con el fin de asegurarlas contra los ataques de los usuarios secundarios. En este trabajo, se propone un método llamado Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) y métodos de defensa de capa cruzada de Levensthein optimizados para asegurar la CRN detectando a los atacantes en dos capas diferentes, la física y la de enlace de datos. Se aplica el modelo de evaluación de confianza de capa cruzada Mean Bid para medir la fiabilidad del usuario secundario por parte de terceros. A continuación, se realiza la clasificación de usuarios maliciosos y normales aplicando el modelo de la Teoría del Juego de Nash Múltiple. Se propone un marco optimizado de centroide cercano (OS-LNCC) para mitigar los ataques de capa cruzada en las CRN. El rendimiento de ambos métodos se evalúa mediante varios parámetros, como el consumo de energía, el tiempo de detección, el retardo de detección, el rendimiento y la precisión de la detección.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Spanisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Las redes de radiocomunicación cognitiva (CRN) han surgido como una alentadora tecnología de red de próxima generación que aborda los problemas relacionados con el acceso dinámico al espectro y la utilización mejorada del mismo de manera significativa. En concreto, los modelos de gestión de la confianza y la reputación y el mecanismo de defensa de capa cruzada son cada vez más considerados para las CRNs con el fin de asegurarlas contra los ataques de los usuarios secundarios. En este trabajo, se propone un método llamado Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) y métodos de defensa de capa cruzada de Levensthein optimizados para asegurar la CRN detectando a los atacantes en dos capas diferentes, la física y la de enlace de datos. Se aplica el modelo de evaluación de confianza de capa cruzada Mean Bid para medir la fiabilidad del usuario secundario por parte de terceros. A continuación, se realiza la clasificación de usuarios maliciosos y normales aplicando el modelo de la Teoría del Juego de Nash Múltiple. Se propone un marco optimizado de centroide cercano (OS-LNCC) para mitigar los ataques de capa cruzada en las CRN. El rendimiento de ambos métodos se evalúa mediante varios parámetros, como el consumo de energía, el tiempo de detección, el retardo de detección, el rendimiento y la precisión de la detección.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Les réseaux radio cognitifs (CRN) sont apparus comme une technologie de réseau de nouvelle génération encourageante qui répond aux problèmes liés à l'accès dynamique au spectre et à l'utilisation améliorée du spectre de manière significative. Les modèles de gestion de la confiance et de la réputation ainsi que les mécanismes de défense intercouche sont de plus en plus considérés pour les CRN afin de les protéger contre les attaques des utilisateurs secondaires. Dans ce travail, une méthode appelée Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) et un cadre de défense intercouche optimisé de Levensthein sont proposés pour sécuriser le CRN en détectant les attaquants sur deux couches différentes, la couche physique et la couche de liaison de données. Le modèle Mean Bid Cross Layer Trust Evaluation est appliqué pour mesurer la fiabilité d'un utilisateur secondaire par un tiers. Ensuite, la classification des utilisateurs normaux et malveillants est effectuée en appliquant le modèle de la théorie des jeux de Nash multiples. Le cadre optimisé de Levesthein Nearest Centroid (OS-LNCC) est proposé pour atténuer les attaques entre couches dans les CRN. Les performances des deux méthodes sont évaluées par différents paramètres tels que la consommation d'énergie, le temps de détection, le délai de détection, le débit et la précision de détection.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Französisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Les réseaux radio cognitifs (CRN) sont apparus comme une technologie de réseau de nouvelle génération encourageante qui répond aux problèmes liés à l'accès dynamique au spectre et à l'utilisation améliorée du spectre de manière significative. Les modèles de gestion de la confiance et de la réputation ainsi que les mécanismes de défense intercouche sont de plus en plus considérés pour les CRN afin de les protéger contre les attaques des utilisateurs secondaires. Dans ce travail, une méthode appelée Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) et un cadre de défense intercouche optimisé de Levensthein sont proposés pour sécuriser le CRN en détectant les attaquants sur deux couches différentes, la couche physique et la couche de liaison de données. Le modèle Mean Bid Cross Layer Trust Evaluation est appliqué pour mesurer la fiabilité d'un utilisateur secondaire par un tiers. Ensuite, la classification des utilisateurs normaux et malveillants est effectuée en appliquant le modèle de la théorie des jeux de Nash multiples. Le cadre optimisé de Levesthein Nearest Centroid (OS-LNCC) est proposé pour atténuer les attaques entre couches dans les CRN. Les performances des deux méthodes sont évaluées par différents paramètres tels que la consommation d'énergie, le temps de détection, le délai de détection, le débit et la précision de détection.