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Нейронные сети и dataset IRIS

С чего всё началось

Я являюсь учеником 4 курса по специальности «информационные технологии» и обучаюсь в институте под названием ЯГТУ.

На последнем семестре у нас появился предмет под названием «Интеллектуальные системы и технологии», в связи с чем я заинтересовался данной темой.

Эта работа будет посвящена анализу данных на основе датасета IRIS а также с использованием таких библиотек как tensorflow, sklearn, ctgan. Естеcnвенно писать код мы будем на Python.

Немного о нейронных сетях

Думаю сильно углубляться в теорию нейронных сетей в данной статье не имеет смысла, информации об этом достаточно в других статьях на этом форуме, или же просто в интернете. Поэтому сосредоточимся на модели сети которую будем использовать.

В данной статье мы будем использовать последовательную нейронную сеть Squental из билиотеки tensorflow.

Sequental модель подходит для простого стека слоев , где каждый слой имеет ровно один входной и один выходной тензор.

В контексте нейронных сетей тензор обычно представляет собой многомерный массив чисел, который используется для хранения и обработки данных. Нейронные сети оперируют тензорами на различных этапах обучения и применения, таких как входные данные (например, изображения, звуки, тексты), веса и смещения (parameters), а также промежуточные активации в различных слоях сети.

Важно отметить, что тензоры в нейронных сетях могут иметь различные размерности. Например:

Тензор ранга 0 - скаляр (например, одно число).
Тензор ранга 1 - вектор (например, одномерный массив чисел).
Тензор ранга 2 - матрица (двумерный массив чисел).
Тензоры более высоких рангов - многомерные массивы чисел.

В процессе обучения нейронных сетей тензоры передаются через слои сети, на которых выполняются математические операции, такие как умножение матриц, применение активаций и т. д. Тензоры также используются для представления градиентов, которые используются для обновления весов в процессе обучения методом обратного распространения ошибки.

Нейрон – базовая единица нейронной сети. У каждого нейрона есть определённое количество входов, куда поступают сигналы, которые суммируются с учётом значимости (веса) каждого входа. Далее сигналы поступают на входы других нейронов. Вес каждого такого «узла» может быть как положительным, так и отрицательным. Например, если у нейрона есть четыре входа, то у него есть и четыре весовых значения, которые можно регулировать независимо друг от друга.

Модель нашей сети будет иметь примерно такой вид:

На схеме ниже нейроны представлены синими точками, связи серыми линиями.

Посмотрев на схему можно увидеть, что у нас есть 2 нейрона на входном слое, 10 на 2, 8 на 3, и 3 нейрона на 4. Параметры сети мы будем подбирать исходя из полученной точности предсказания при тестах. Эта нейросеть является полносвязной, т.е. каждый нейрон слоя, связан со всеми нейронами предыдущего слоя. Такие слои имеют название Dense.

Мы будем использовать именно такую модель, так как она эффективна при работе с данными, где важна каждая особенность, независимо от её местоположения во входных данных.

Анализ данных

Для начала загрузим все необходимые библиотеки, чтобы в дальнейшем на это не отвлекаться.

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn import metrics
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from sklearn.metrics import confusion_matrix, silhouette_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from ctgan import CTGAN
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score

Итак начнём работу с данными. Для этого загружаем сам датасет.

iris = datasets.load_iris()

В этом датасете содержится информация о 3 различных видах ирисов и их параметров, всего 150 цветов, по 50 примеров на цветок соответственно.

Виды цветков:

Iris setosa
Iris versicolor
Iris virginica

Характеристики цветков:

Длина чашелистика (sepal length) в сантиметрах.
Ширина чашелистика (sepal width) в сантиметрах.
Длина лепестка (petal length) в сантиметрах.
Ширина лепестка (petal width) в сантиметрах.

Проведя исследования на стандартном наборе параметров, я пришёл к выводу что данных недостаточно для хорошего обучения нейросети, поэтому мы сгенерируем синтетические данные для масштабирования исследования.

Для этого будем использовать библиотеку CTGAN

Мы берём отдельно данные по каждому виду цветка, генерируем значения в количестве 2000 штук для каждого цветка, соединяем в один массив и заменяем изначальные данные.

iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
iris_df['species'] = iris.target_names[iris.target]

# Выбор данных для каждого типа ириса
setosa = iris_df[iris_df['species'] == 'setosa']
versicolor = iris_df[iris_df['species'] == 'versicolor']
virginica = iris_df[iris_df['species'] == 'virginica']
#Создание модели нейросети
ctgan = CTGAN(
    epochs=50, # Эпохи обучения, тут главное не переобучить нейросеть
)
ColSamples = 2000
# Обучение модели на основе данных о setosa. Не забудем что должны убрать последнюю 
#колонку, т.к. в ней содержится просто название цветка.
ctgan.fit(setosa.iloc[:,:-1])
# Генерация синтетических данных
setosa = ctgan.sample(ColSamples)
ctgan.fit(versicolor.iloc[:,:-1])
# Генерация синтетических данных
versicolor = ctgan.sample(ColSamples)
ctgan.fit(virginica.iloc[:,:-1])
# Генерация синтетических данных
virginica = ctgan.sample(ColSamples)

iris.data = np.concatenate((setosa, versicolor, virginica))

Далее мы также должны создать массив из такого же количества 0, 1 и 2. Так как по этим маркером будет определяться к какому виду принадлежат те или иные значения.

# Создание массива с ColSamples нулями
zeros = np.zeros(ColSamples, dtype=int)

# Создание массива с ColSamples единицами
ones = np.ones(ColSamples, dtype=int)

# Создание массива с ColSamples двойками
twos = np.full(ColSamples, 2, dtype=int)

# Объединение массивов в один
new_data = np.concatenate((zeros, ones, twos))
# Объединение существующего датасета с новыми данными
iris.target = new_data
# На всякий случай отсортируем
iris.target = np.sort(iris.target)

Далее создаём макет, с которым и будем работать в дальнейшем

iris_frame = DataFrame(iris.data)
# Делаем имена колонок такие же, как имена переменных:
iris_frame.columns = iris.feature_names
# Добавляем столбец с целевой переменной:
iris_frame['target'] = iris.target
# Для наглядности добавляем столбец с сортами:
iris_frame['name'] = iris_frame.target.apply(lambda x: iris.target_names[x])
# Смотрим, что получилось:
print(iris_frame)

Выведем график для оценки распределения данных

plt.figure(figsize=(20, 24))
plot_number = 0
for feature_name in iris['feature_names']:
    for target_name in iris['target_names']:
       plot_number += 1
       plt.subplot(4, 3, plot_number)
       plt.hist(iris_frame[iris_frame.name == target_name][feature_name])
       plt.title(target_name)
       plt.xlabel('cm')
       plt.ylabel(feature_name[:-4])
plt.show()

Пока что-либо сказать сложно, но можно заметить, что распределение у некторых значений похоже на нормальное.

Далее для более наглядного результата построим таблицу зависимостей

import seaborn as sns
sns.pairplot(iris_frame[['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)','name']], hue='name')
plt.show()

График зависимостей

По этому графику мы точно можем сказать что значения petal length и petal width имеют очень хорошую корреляцию и их распределение похоже на нормальное. А также можно заметить, что кластеризация этих значений явно выражена что также немаловажно при обучении модели. Поэтому принимаем решение работать именно с этими значениям, чтобы в конечном итоге получить хороший результат.

Давайте убедимся в верности суждений графически

corr = iris_frame[['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']].corr()
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True
with sns.axes_style("white"):
   ax = sns.heatmap(corr, mask=mask, square=True, cbar=False, annot=True, linewidths=.5)
plt.show()

Видим что коэффициент корреляции составляет 0.9 у petal length и petal width.

Отмечу, что работаем мы с синтетическими данными, поэтому каждый раз набор значений у нас будет разный, но за все прогоны этот коэффициент всегда оставался самым большим.

Оценивать много это или мало можно примерно по этим значениям:
До 0,2 — очень слабая корреляция
До 0,5 — слабая
До 0,7 — средняя
До 0,9 — высокая
Больше 0,9 — очень высокая

Разбиваем данные для работы с моделью нейросети

train_data, test_data, train_labels, test_labels = train_test_split(
    iris_frame[['petal length (cm)', 'petal width (cm)']],
    iris_frame[['target']],
    test_size=0.3, #Это значение влияет на то какое количество данных пойдёт на 
  # обучение модели, а какое на тестирование. соответсвенно на обучение 0.7 всех
  # данных, а 0.3 на тестирование, считаю это отношение оптимальным в этом случае
  
    random_state=0 
)

Строим линию регрессии для наших данных

fit_output = stats.linregress(iris_frame[['petal length (cm)','petal width (cm)']])
slope, intercept, r_value, p_value, slope_std_error = fit_output
print(slope, intercept, r_value, p_value, slope_std_error)

Она минимизирует сумму квадратов расстояний между этой линией и каждой точкой данных. Таким образом, линия регрессии может использоваться для предсказания значения зависимой переменной для заданных значений независимой переменной. Т.е буквально показывает что при длине листа 2 его ширина будет равна 0.5

Видим, что для такого объёма данных, они достаточно неплохо структурированы, и линия регрессии повторяет общую картину распределения точек.

Думаю рассматривать тут SGDClassifier и методы Kmeans, не будем, не очень они подходят для анализа такого объём данных а результаты оставляют желать лучшего, поэтому приступим к самому интересному.

Создание и обучение нейросети

Оно происходит следующим образом, в датасете каждого набора параметров которые относятся к одному ирису, также относится 0, 1 или 2
Соответственно у нас для:

Iris setosa - 0
Iris versicolor - 1
Iris virginica - 2

И обучение и предсказание происходит следующим образом: алгоритм понимает какие значения относятся к 0, какие к 1, а какие к 2 и при предсказании на выходе мы получаем обычный массив из этих чисел:

[2 1 0 2 0 2 0 1 1 1 1 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0 2 1 1 2 0 2 0 0]

На входе: [2 1 0 2 0 2 0 1 1 1 2 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0 1 1 1 2 0 2 0 0] +
параметры цветка:

[[-0.18295039 -0.29318114]
[ 0.93066067 0.7372463 ]
[ 1.04202177 1.63887031]
[ 0.6522579 0.35083601]
[ 1.09770233 0.7372463 ]
[ 0.03977182 -0.16437771]
[ 1.26474398 1.38126345]
[ 0.48521625 0.47963944]]

Создание модели

#Создаём модель нейросети, задаём количество нейронов и т.п.
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(2,))
    # первый слой содержит 64 нейрона, а входной input_shape 2(по количеству входных значений)
    tf.keras.layers.Dropout(0.1),
    #  Dropout - это метод регуляризации, который случайным образом удаляет некоторые нейроны в процессе обучения.
    #  Это помогает предотвратить переобучение модели, улучшая ее обобщающую способность.
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(8, activation='relu'),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(3, activation='softmax')  # Выходной слой с 3 нейронами (по числу классов) и функцией активации softmax для многоклассовой классификации
])

# Компиляция модели
model.compile(optimizer='adam',  # Оптимизатор Adam это метод оптимизации, который сочетает в себе идеи метода 
                                  #градиентного спуска с адаптивным шагом обучения и метода адаптивной оценки моментов.
              loss='sparse_categorical_crossentropy',  # Функция потерь для многоклассовой классификации
              metrics=['accuracy'])  # Метрика - точность классификации

# Обучение модели на train_data указываем количество эпох(50), главное не переобучить модель
history = model.fit(train_data, train_labels, epochs=50, batch_size=2, validation_split=0.2, verbose=1)
# batch_size=2 Это количество образцов данных, передаваемых модели за один раз перед обновлением весов. Меньшие значения обычно означают 
# более стабильное обучение, но могут привести к более длительному времени обучения.
# Оценка модели на test_data
test_loss, test_accuracy = model.evaluate(test_data, test_labels, verbose=1)
print("Test Accuracy:", test_accuracy)

predictions = model.predict(test_data)

# Предсказанные метки классов (индексы классов с наибольшей вероятностью)
predicted_classes = predictions.argmax(axis=-1)

# Вывод предсказанных меток классов
print("Predicted classes:", predicted_classes)

Получаем в итоге: Test Accuracy: 0.9988889098167419
Можно сделать вывод что результат просто отличный, т.е. при работе нейросети ошибка будет составлять всего 0.1 процент.
Давайте убедимся в этом построив графики с изначальными значениями, и предсказанными

color_dict = {0: 'red', 1: 'green', 2: 'blue'}
plt.figure(figsize=(12, 6))

# Исходные данные
plt.subplot(1, 2, 1)
for idx, true_label in enumerate(test_labels['target']):
    plt.plot(test_data.iloc[idx]['petal length (cm)'], test_data.iloc[idx]['petal width (cm)'], 'o', color=color_dict[true_label])
plt.ylabel('petal width (cm)')
plt.xlabel('petal length (cm)')
plt.title('Тестовые данные для предсказания нейронной сетью')

# Вывод кластеризованных данных
plt.subplot(1, 2, 2)
for idx, pred_label in enumerate(predicted_classes):
    plt.plot(test_data.iloc[idx]['petal length (cm)'], test_data.iloc[idx]['petal width (cm)'], 'o', color=color_dict[pred_label])
plt.ylabel('petal width (cm)')
plt.xlabel('petal length (cm)')
plt.title('Предсказанные значения')

plt.show()

По графику видно что ошибка всего в двух точках при предсказании составляет
2/(6000*0,3) = 0,00111111 или 0,1111%
При такое погрешности можно считать что наша модель является весьма неплохой и имеет место быть.
Также графически мы можем оценить обучение модели

if history is not None:
    # Оценка модели на тестовом наборе данных
    test_loss, test_accuracy = model.evaluate(test_data, test_labels, verbose=1)
    print("Test Accuracy with Dropout:", test_accuracy)

    # Построение кривых обучения и валидации
    plt.plot(history.history['accuracy'], label='accuracy')
    plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.ylim([0, 1])
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')
    plt.show()
else:
    print("Training history is not available.")

График обучения и валидации представляет собой визуализацию изменения точности (accuracy) модели на протяжении процесса обучения.

Точность на обучающем наборе данных:
Линия accuracy показывает, как меняется точность модели на обучающем наборе данных в течение каждой эпохи обучения. Она отражает, насколько хорошо модель учится на обучающих данных. По мере того как обучение продолжается, точность на обучающем наборе может увеличиваться, но иногда может наблюдаться переобучение, когда точность на обучающем наборе продолжает расти, а точность на валидационном наборе данных начинает уменьшаться.
Точность на валидационном наборе данных:
Линия val_accuracy показывает, как меняется точность модели на валидационном наборе данных в течение каждой эпохи обучения. Валидационный набор данных не используется для обучения модели, но он используется для оценки производительности модели во время обучения. Это помогает определить, насколько хорошо модель обобщает данные, которые она не видела во время обучения. По мере того как обучение продолжается, мы хотим видеть, чтобы точность на валидационном наборе данных увеличивалась, и в идеале приближалась к точности на обучающем наборе данных.

Оригинальный датасет IRIS

Для сравнения покажу что у меня получилось при работе с оригинальным датасетом.

Оригинальные данные имеют следующий вид:

Как видно здесь так же присутствует корреляция между petal length и petal width

Теперь же попробуем тот же способ обучения нейросети, только количество эпох я изменил на 100, в связи с недостаточным количеством данных для обучения

Test Accuracy with Dropout: 0.9777777791023254
Получаем следующие цифры и видим погрешность в 1 точку в данном случае
1/(150*0.3) = 0,0222 или 2,22% что уже является весомым числом при при анализе данных.

Оценка обучения модели графически

Как видим график уже не такой ровный и аккуратный, что опять же связываю с недостаточным количеством данных. Так же к конце графика мы видим что val_accuracy находится ниже accuracy что явно связано с переобучением модели, попробуем увеличить количество валидационных данных до 0.3 и уменьшить количество эпох до 60

Получаем такой результат, это уже больше походит на правду. При этом график предсказанных значений не изменился, точность получилась следующая 0.9777777791023254. Я провёл пару десятков тестов c разными значениями и моделями, и большей точности с данным набором данных добиться не удалось.

Но в любом случае предсказание значений с помощью нейросети является более точным по сравнению с скажем методом кластеризации KMeans который использует метод k-средних, который заключается в минимизации среднеквадратичного отклонения точек данных от центров кластеров.

model = KMeans(n_clusters=3)#
model.fit(train_data)
model_predictions = model.predict(test_data)
print("Точно при кластеризации KMeans:", metrics.accuracy_score(test_labels, model_predictions))
print("Метрики для KMeans:\n", metrics.classification_report(test_labels, model_predictions))

При таком подходе нужно изначально задавать примерные центра кластеров, потому что алгоритм в данном случаем при вычислении алгоритм не понимает где какой именно кластер, и например кластер со значениями 0 может принять значения 1 а 1 принять значения 0 как на рисунке выше. Так как из-за этого точность считалась не корректно то можем на глаз увидеть, что не правильно отобразились 2 точки, следовательно погрешность примерно 2/(150*0.3) = 0.044 = 4.4%

При применении метода KMeans на изменённом мной датасете IRIS точность этого метода сильно ухудшалась и была равна 70-80%

Вывод

Сегодня мы ознакомились с обучением нейронной сети с помощью библиотеки tensorflow. Выяснили какие методы лучше использовать при обработке данных, а так же графически
отобразили, как сильно зависит точность модели от исходных данных, и можем сделать вывод, что чем лучше данные кластеризованы, то тем точнее будет наша модель.

Отмечу что приведение данных к виду, в котором они будут пригодны для работы будет дольше чем построение любой из описанных моделей.

Это моя первая подобная работа, прошу писать любую критику, и что можно доработать.

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14 февр. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

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26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

Although recovering from the terrible effects of investment fraud can seem like an impossible task, it is possible to regain financial stability and go on with the correct assistance and tools. In my own experience with Wizard Web Recovery, a specialized company that assisted me in navigating the difficulties of recouping my losses following my fall prey to a sophisticated online fraud, that was undoubtedly the case. My life money had disappeared in an instant, leaving me in a state of shock when I first contacted Spartan Tech Group Retrieval through this Email: spartantechretrieval (@) g r o u p m a i l .c o m The compassionate and knowledgeable team there quickly put my mind at ease, outlining a clear and comprehensive plan of action. They painstakingly examined every aspect of my case, using their broad business contacts and knowledge to track the movement of my pilfered money. They empowered me to make knowledgeable decisions regarding the rehabilitation process by keeping me updated and involved at every stage. But what I valued most was their unrelenting commitment and perseverance; they persisted in trying every option until a sizable amount of my lost money had been successfully restored. It was a long and arduous journey, filled with ups and downs, but having Spartan Tech Group Retrieval in my corner made all the difference. Thanks to their tireless efforts, I was eventually able to rebuild my financial foundation and reclaim a sense of security and control over my life. While the emotional scars of investment fraud may never fully heal, working with this remarkable organization played a crucial role in my ability to move forward and recover. For proper talks, contact on WhatsApp:+1 (971) 4 8 7 - 3 5 3 8 and Telegram:+1 (581) 2 8 6 - 8 0 9 2 Thank you for your time reading as it will be of help.
27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

Online crypto investment can seem like a promising opportunity, but it's crucial to recognize that there are no guarantees. My experience serves as a stark reminder of this reality. I was drawn in by the allure of high returns and the persuasive marketing tactics employed by various brokers. Their polished presentations and testimonials made it seem easy to profit from cryptocurrency trading. Everything appeared to be legitimate. I received enticing messages about the potential for substantial gains, and the brokers seemed knowledgeable and professional. Driven by excitement and the fear of missing out, I invested a significant amount of my savings. The promise of quick profits overshadowed the red flags I should have noticed. I trusted these brokers without conducting proper research, which was a major mistake. As time went on, I realized that the promised returns were nothing but illusions. My attempts to withdraw funds were met with endless excuses and delays. It became painfully clear that I had fallen victim. The reality hit hard: my hard-earned money was gone, I lost my peace of mind and sanity. In my desperation, I sought help from a company called DEFTRECOUP. That was the turning point for me as I had a good conversation and eventually filed a complaint via DEFTRECOUP COM. They were quite delicate and ensured I got out of the most difficult situation of my life in one piece.
04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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19.12.24 17:07 rebeccabenjamin

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